LaTeX to WordPress

Phillip Lord

1 Introduction

About a month ago, we managed to get funding from JISC for knowledgeblog; the idea is to turn a blog platform from something for light commentary into a framework for serious scientific publication. One of the key requirements for this is to fit in with peoples existing working practices; and for this, we need a good document creation environment. This means word and latex. I’ve been working mostly on the latter, and this post is the first outcome. It’s generated totally automatically from latex. This is an advance on my paper on realism which was semi-automatically converted, with some hand editing of the HTML.

At the moment, the tool-chain is a little bit clunky, but it will improve! This is not meant to be an annoucement that all is ready, just an early alpha release and proof-of-principle.

2 Implementation

The implementation of these tool-chain uses three pieces of software:

latextowordpress:

This package, that I have written, uses plasTeX to parse and render the latex into HTML. Most of the work is being performed by plasTeX out-of-the-box, although using a non-default configuration. Math-mode is being treated separately however, rather than using plasTeXs default image rendering approach.

blogpost:

blogpost is being used to actually post the generated HTML onto the web. The HTML can also be cut-and-paste directly into wordpress, but blogpost is easier for me, as its the usual tool I use anyway (normally over asciidoc source). Blogpost is unmodified.

mathjax-latex:

This is a wordpress plugin, that I have written, which uses MathJax to render math-mode from the original latex in the browser. The plugin just injects the mathjax javascript headers into a post on-demand (i.e. only on posts with math-mode in them).

Currently, this is all held together with some dodgy makefiles; this will be improved in time.

The first and last of these tools are available from knowledgeblog. I’ve tested them on Ubuntu 10.04 and they are in alpha. Comments are welcome, to knowledgeblog-discuss.

3 Key Features

At the moment, I haven’t fully explored all the features of LaTeX that are well supported. However, all the structural elements (sections, lists), bibliographies, links via the hyperref package all seem to work well.

The math mode rendering works well. I’ve been using one famous equation: \(E=mc^2\), as my main test. But more complex examples work also. This is from mathjax:\(J_\alpha (x) = \sum _{m=0}^\infty \frac{(-1)^ m}{m! \, \Gamma (m + \alpha + 1)}{\left({\frac{x}{2}}\right)}^{2 m + \alpha }\).

I’ve made a few tweaks to this also for common idioms. So the lesser than symbol is written in mathmode in latex but rendered directly in HTML: <.

4 Future Work

There are many things left to do yet. The process needs to made smooother, with a single tool to hook the current tool-chain together; it would be good to attach a PDF generated from the latex also. Currently, titles are set independently (which is why this post appears to have two titles). The mathjax plugin needs configuration options (it overwrites wp-latex functionality at the moment). And there is significant testing to do to see what advanced features (figures critically!) work and don’t work. Still, it’s good to see that most of the tools that I needed to get this work already existed. With luck, most of the other tools we need will be as good.

To me the difficulty with institutional repositories is this. First, they are a resource. Then, some one says, this is good, everyone should do this. Then, someone else says, hey this is great, we could use this for our RAE (REF, whatever) return.

Now, you have to deposit things in your IR. But people object, on various “data is mine” grounds, so perhaps they make the IR non-public. The data model gets tweaked with various additional data (which school, who your line manager is) necessary for RAE. At the same time, your co-authors also have to deposit into their IR. And, if you move, you have to type your entire back catalogue into various repositories for your new institution.

Currently I am supposed to deposit papers in various IRs, including at University and school level. As well as add bibliographic information to various databases. And, then of course, project wiki’s. And the funders want the information in various databases. All of which is very time consuming, produces highly duplicated, and often error-prone data. In short, it’s a bad thing.

The irony is, if you google for any of my papers, the main source from which they are scraped is my website. I set this up myself many years ago now; it’s a simple bibtex to HTML thing (actually not so simple nowadays — it grew over time). So, the simplest and most straight-forward solution, also turns out to be the best. The most important thing is this; the bibtex files are the ones that I use, for my own work, for citing myself (which, like any good scientist I do as often as possible even when the citation is largely irrelevant). The website is what I use, when on the road to get the PDF of my own papers; if I want to give a reference to someone, I’ll email a link to my website. So, I keep it upto date, because it’s in my benefit to do so.

We need a few simple and easy to use standards for bibliographic data. It has to be simple, because it needs to fit in with peoples’ current work practices; this means it needs to be supported by a heterogenous environment, by many different tools. And it’s won’t be, if the standard is hard to develop against.

For data, of course, the issues are somewhat different. Mostly because data needs more structure than human-readable information, and because the data is often large. However, two issues remain: first, we still need to fit with peoples working practices; second, with data, engaging in the institutional football we see with bibliographic data, will still be a bad thing.

Again, simple data standards are what we need. After that, people will choose whatever they choose; the data standard will be enough to bring it all together in the best way that we can.

I’m very pleased that our grant for knowledgeblog has been accepted by JISC. I shall follow the tradition that I set with my last post, of publishing all my primary scientific output on this blog. In this case, I’m using Word, which like the latex that I used last time isn’t perfect. Still improving this process is part of the knowledgeblog proposal, so this post is also attacking a key deliverable for the grant!

The main content for this post is also available on the knowledgeblog events blog.

Outline Project Description

The project extends existing blogging tools for use as a lightweight, semantically linked publication environment. This enables researchers to create a hub in the linked-data environment, that we call knowledge or k-blogs. K-blogs are convenient and straight-forward for authors to use, integrating into researchers existing work practices and tools. The provide readers with distributed feedback and commenting mechanisms. We will support three communities (microarray, public health and workflow), providing immediate benefit, in addition to the long term benefit of the platform as a whole. Additionally, this will enable a user-centric development approach, while showcasing the platform as the basis for next generation research publishing. 1. Introduction

¹This document describes a proposal for a project within the JISC “Managing research Data” call. Data comes in many forms, from raw statistics, to highly structured databases, through to textual reports; natural language, although hard to search and manage, is still the richest form of representation; data in the form of reports and publications are the central hub around which all other data sit. This project, therefore, will provide a lightweight, yet extensible, framework for scientific publishing, incorporating a software-supported peer-review process. Bi-directional links will be maintained both between publications and to other forms of data, using semantic markup to enhance the meaning of these links. We will also customize this framework for three communities which, as well as being directly useful, will provide real-world requirements. The project will largely develop “glue” between existing, widely-used, open-source software systems, ensuring its sustainability and usefulness past the end of the funding.

2. Fit to Programme Objectives and Project Outline

²The project call identifies the complexity and hybrid nature of the UK research data environment; despite this, one central focal point remains — most researchers spend considerable amounts of time discussing their data in the form of “paper” publications. For some, more theoretical disciplines, such as parts of computer science, the paper is the sole output; in others, such as biology, datasets are associated with papers and the barriers between “publication” and “data” are breaking down; most data sources in biology are rich in annotation; text that supports and explains the raw data. It is normally the annotation, not the raw data, which defines the quality of the resource. In these cases, text is an intrinsic part of the data.

³However, the conventional publication process has changed relatively little; the adoption of web technologies have largely been used as a distribution mechanism. Publications are still expensive — either at subscription or publication time, depending on the business model of the publisher, and involve considerable, time-consuming interactions between author and publisher, often relating to display and presentation issues. This is in stark contrast to, for example, the biological data centres where both raw and annotated data are often made available within hours of their generation.

⁴This situation is unfortunate because it limits the ability of researchers to customise their publication process for the requirements of their own discipline. As demonstrated by Shotton et al, and Rousay et al, it is possible to add considerable value, both enhancing the paper for the reader, as well as providing direct and semantically enhanced links to underlying data. The cost of the existing process, however, makes this form of publication unlikely for some data; for example, few scientists publish papers about negative results, resulting in an acknowledged publication bias^,. As a result, it is hard for the semantically enhanced publication to take its place as the central hub for a linked data environment as envisioned by Coles and Frey, linking to and between research datasets, and the published knowledge about these datasets.

⁵In the last decade, the blog has become a common, web-based publication framework. There are now numerous off-the-shelf tools and platforms for managing blogs, providing a high-degree of functionality. Many scientists blog about their work, about other published work (research blogging) or “live blog” about conferences and talks as they happen. In this case, the researcher is in-charge of their own publication environment, can extend it to their requirements, and publication happens immediately. However, the blog has not yet become a standard means of publication for primary research output.

⁶Recently, as part of the EPSRC funded Ontogenesis network (ref), we trialled the Knowledge Blog process; in this case aimed at producing an educational resource describing many aspects of ontology development and usage, which might previously have been published in book form. We have shown that with this technology base, it is possible to replicate many of the features of the open peer-review, scientific book publication process; following two small meetings, we have written around 20 articles, and the website maintains around 1000 post reads per month (not simple hits!). To achieve this, we used only two features of the blog — trackbacks (bidirectional links) and categories (hierarchical keywords); although we used the WordPress blogging software, these features are supported by most other systems. We call these articles k-blogs.

⁷Currently, however, the k-blog process is not fully supported with blog software alone, nor does it fully support the referencing, advanced linking and provenance needed specifically for research publications. For this project, we propose to provide extensions to support data-rich publications, deeply and semantically linked to other k-blogs and to other forms of data repository. Therefore, the project addresses the objectives and aims of the call through four main workpackages.

1) A documented k-blog process (WP1.1) describing different levels of  peer-review suitable for different forms of research data. An implementation (WP1.2), the k-blog platform, of these process based around open-source, off-the-shelf software.

2) Extensions to the k-blog platform supporting linking. This includes full support for referencing including COINS metadata on posts (WP2.1), client-side and permanently linked versions (WP2.2) and bidirectional links (WP2.3) to other data sets. We will add semantics to these links using the Citation Ontology (CiTO) (WP2.4).

3) Support for three specialist environments—healthcare (WP3.1), microarray (WP3.2) and workflows (WP3.3). All useful in their own right and showcasing the extensibility of the framework.

4) Documentation and tooling to integrate the k-blog process into scientists existing working practice and tooling; scientists will be able to publish from Word, OpenOffice, Google Docs or LaTeX (WP4.1). We will add tooling and documentation, as WP4.2, to support the use of reference management tools such as Endnote, Mendeley or Zotero, making use of deliverables from WP2.

3. Quality of proposal and Robustness of Workplan

3.1 WP1: Knowledge Blog Process

⁸In this project, we aim to develop a light-weight publication framework, including the desirable aspects of the formal peer-review process. However, different forms of scientific publication require different levels of peer-review. For example, for http://ontogenesis.knowledgeblog.org, we require two reviews from an editorial board, assessing quality, appropriate for an educational resource. However, for http://process.knowledgeblog.org, which is intended to contain informal “how-to” and request for comment documents, a much lighter-weight, single editorial review assessing scope alone is more appropriate. Deliverable WP1.1 will consist of documentation describing both formally and informally, a number of levels for the knowledge blog process, and how these can be achieved using a blog. These documents will, themselves, be published on http://process.knowledgeblog.org.

⁹These processes will be implemented as Deliverable WP1.2, comprising freely available and widely used pieces of software, with additional “glue”. The basic publication framework will use WordPress 3 (WoP) — an open-source, multi-site, multi-author blogging system used to provide the hosted blog service at http://www.wordpress.com. While, we have found that WoP supports many aspects of this process, particularly from the readers perspective, a significant degree of “book-keeping” is required from authors, reviewers and editors. Readers know whether a paper has been reviewed or not, but authors have to remember for themselves who is reviewing the paper. Therefore, we will use a “ticket system”, specifically Request Tracker 3 (RT) (http://bestpractical.com/rt/). Both WoP and RT are extensible with plugins and will be extended and adapted to reflect the k-blog levels of WP1.1.

¹⁰We will use this extensibility to provide a light-weight integration. RT operates as an email response system; by extending WoP to send email on submission of new papers, this can provide both an integration point, as well as the main point of interaction for authors, reviewers and editors. To provide editorial and reviewer functionality tickets can be moved between queues; extensions to RT will use standard blogging XML-RPC calls to feedback to WoP by, for example, re-categorising papers once accepted. OpenID (http://openid.net) will be used to integrate the user accounts between the two systems. WoP already supports this fully, while RT supports it in skeleton form.

¹¹Although we will provide an implementation of the k-blog process, it will be described sufficiently generically to support complete and independent implementation.

3.2 WP2: References and Metadata
¹²For k-blogs to become an integral part of the scientific record, they must fully support the semantic and linked data environment. Although WoP supports standard URI based linking to resources, and bidirectional “trackback” linking to other resources, it lacks complete functionality suitable for research communities. This is a rare example of functionality that is not already provided by WoP or an associated plugin. Deliverable WP2.1 will fulfil this need; we will support the insertion of at least DOIs and PubMed IDs (PMID), that will be resolved to full human-readable reference lists for display, using APIs provided by CrossRef and NCBI eUtils respectively. To fully support computational agents wishing to access the same information, references will also support COinS metadata, embedded into the display HTML.

K-blog posts will also require outward facing metadata, that describe the resources they provide in a standards-compliant manner. The Open Archives Initiative (OAI) provide standards that aim to facilitate the efficient dissemination of content. Specifically, the Object Reuse and Exchange specification (OAI-ORE) is a standard for the description and exchange of compound digital objects  (such as a WoP post or page). The WordPress OAI-ORE plugin provides link header elements that implement this specification.

¹³Our initial investigations into the k-blog process showed that WoP support for versioning and provenance are lacking; the k-blog process involves updating papers after submission but before final acceptance. While WoP stores all these versions, these are only currently visible by authors or editors through the administration interface. Whilst existing plugins for WoP already provide some of this functionality, Deliverable WP2.2 will uncover these to readers, along with a defined permalink scheme for access to all versions, providing full provenance.

¹⁴WoP supports bi-directional links in the form of trackbacks; this is mediated by XML-RPC calls between resources when a link is made. This will support linking to data where, for example, the data is another k-blog; however, general data resources may lack support for this process. Therefore, as Deliverable WP2.3, we will provide a trackback proxy, hosted on the http://knowledgeblog.org server, storing and presenting these links for resources that cannot directly process trackbacks.

¹⁵To complete this work package, we will add semantics to the links using CiTO, as Deliverable WP2.4. Therefore, as well as enabling easier data linking and provenance, we will also enable addition of meaning to these links.

3.3 WP3 – Specialist Environments

¹⁶The k-blog platform and process is designed to be flexible and adaptable to the needs of specialist environments. We will use three main use cases to ensure real world applicability of the software, as well as fulfilling the immediate needs of these communities.

¹⁷For Deliverable WP3.1, we will add additional features for supporting the microarray community. Currently, the microarray community is well serviced in terms of metadata capture (MIAME) and deposition in public repositories (ArrayExpress, GEO). As part of WP2, we will support linking to these datasets through stable URIs. However, these resources deal only with data generation. Post-processing and analysis is largely captured at the publication stage, often in supplementary material.

¹⁸A substantial amount of this analysis uses BioConductor: a widely used, open-source platform for statistical microarray analysis based on the R statistical programming language. We will extend k-blog with specific support for R and BioConductor. Authors will be able to directly embed code into k-blog papers, along with the figures that result; as a result reviewers and readers will be able to see a computationally precise description of methods and replicate the generation of figures should they choose.

¹⁹Finally, we will investigate the possibility of publication to a k-blog using only R code and references to public databases, in a process similar to Sweave — figures will be generated on the server, provide guarantees of correctness and precise provenance. The limited scope of this call means this part of WP3.1 will be proof-of-principle only.

²⁰For WP3.2, we will focus on the public health community (PHC): a key workforce in delivering quality and effective healthcare by providing timely and accurate public health intelligence (PHI)^,. PHI is a varied environment performing statistical analyses: producing information figures, diagrams and reports to communicate results to the wider health community. However, the PHC operates in small groups with little knowledge networking. The main aim of the k-blog is to improve the availability of health information, data and knowledge, to inform decisions for health protection and care standards as supported by the Quality Improvement Productivity and Prevention initiative. The NWeHealth e-Lab project, hosted at The University of Manchester, provides an environment to bring together research objects into a single location. As elsewhere, textual data forms the key hub that links together all the other forms of knowledge. By linking to e-Lab research objects from a k-blog, this link will be made explicit, available, interpretable and directly valuable to the PHC; as a result WP3.2 is synergistic with the rest of the proposal. This community also bring a set of access control requirements. To support these we will use existing WoP facilities, providing a simple, easy-to-use three level access model.

²⁰For WP3.3, we will generate k-blog content about Taverna workflows and methods for building them. Workflows have become a popular way of realizing computational analyses and have become an important form of data. The JISC funded myExperiment project is widely used to disseminate the workflows themselves. Knowledge about issues surrounding workflows is, however, more difficult to produce and disseminate. A k-blog, with its ability to produce short, targeted articles as the need arises and the resources become available for writing, suits the need for taverna workflow documentation. We will seek k-blogs on Taverna issues such as: the basics of workflow design; how to choose among a set of similar services in producing a workflow; and, the testing of workflows. We will implement a light-weight mechanism, using trackbacks, to link between the k-blog and myExperiment.

²¹As part of WP3, we will also hold four workshops, at 3-month intervals, each focusing on one particular k-blog and community. These workshops will be of the form previously trialled as part of the Ontogenesis network, and will serve several purposes; requirements gathering and feedback for us, education for the community and development of content, that demonstrates the process to the general readership.

3.4 WP4 – Integration with Existing Working Practices

²²For the k-blog process to be acceptable to communities such as those described in WP3, it must fit with existing working practices. Researchers mostly write documents using a word-processor. Fortunately, as the k-blog platform is based on the widely-used WoP, which in turns offers a widely-supported API, this style of working can be readily integrated. It is already possible to author using Word (2007 onward), OpenOffice, Google Docs and LaTeX using integrated or existing technologies, as demonstrated by our previous work at http://ontogenesis.knowledgeblog.org. For Deliverable WP4.1, user oriented documentation, describing these tools will be developed. This documentation will also describe clearly how to present and organise papers in a way which is optimized for the k-blog process. While, we expect this documentation to take a significant time-span to produce, refining it as a result of user feedback, it is important to note that a k-blog is already useful and possible.

To take maximal advantage of linking technologies developed in WP2, we will need to integrate with existing technologies for referencing. As deliverable WP4.2, we will add tooling to enable the use of bibliographic tools such as Endnote, Mendeley, Zotero or BiBTeX to insert references that k-blog can directly translate. Largely, this should consist of “styles”, modifying the in-text citation, as the reference plugin of WP2.1 will generate reference lists. As with other deliverables, this tooling will include substantial documentation, developed using the k-blog process.

4. Project Timeline

5. Project Management Arrangements

²³The project will be managed from Newcastle University; the primary management will be from Dr Lord who will be responsible for:

• Developing Project Management Plans;
• Ensuring that the Project technical objectives are met;
• Prioritising and reconciling conflicting opportunities;
• Reporting and collaborating with JISC programme Manager;
• Dissemination of the k-blog platform.

Project progress will be evaluated through scheduled, short, “stand-up” meetings on a weekly basis, conducted face-to-face, via skype or phone as appropriate. Although most project staff are co-located, primary unscheduled communication will be via public mailing list, ensuring maximum visibility and openness. User consultation will be via public mailing list, as well as through a “dogfooding” k-blog. All project staff have been handpicked; they are highly experienced and self-directed, as outlined elsewhere. All are associated with several other projects and duties (research, research support, teaching and training), and are responsible for managing these independent workloads.

5.1 Risks

²⁴Staff Risk – as with all projects, loss of staff could negatively impact on this project; however, all staff are on permanent contracts, have long histories in research, so this is less likely. Additionally, by dividing the work between five individuals, we limit the risk should a single person leave.

WoP3 and other dependencies – the project depends on other software, most notably WoP for which a new version (3.0) is now in beta; however the software is widely supported. Other software is replaceable.

Standards Shifting – the project depends on a number of standards and these may change. In this project, we will NOT support standards, but rather use those that support us. Where standard change rapidly, their implementation will be delayed (till they stabilize) or dropped. None of the standards described here is critical to the success of the project.

5.2 IPR Position

²⁵All code will be developed under open source licences. WoP and RT are licensed under GPL, so code linking to these will be likewise licensed. Code that is separable will be released under LGPL. Code will remain copyright of respective institutions or authors. Any documentation produced by project staff relating to the project will be licensed under Creative Commons Attribution license. Licensing of individual k-blogs will be delegated, but permissive licenses will be encouraged.

5.3 Sustainability

²⁶This project is largely based around innovative, novel and leading use of existing software. As such the sustainability of the majority of the technology base is not dependent on project members but large companies with established and proven business models. The k-blog process will be cleanly separated from its implementation, ensuring only weak dependencies to underlying software. Where, we produce software “glue”, public and widely supported APIs will be used where possible. This will ensure that components are replaceable. All code, including historical versions will be publicly available. Documents produced by project staff will be publically available and clearly licensed so will be archived through the internet “cloud” resources; we are also seeking explicit support for archiving from the British Library.

5.4 Staff Recruitment

²⁷All staff are already in post.

5.5 Key Beneficiaries

²⁸Our key beneficiaries are the public health, microarray and workflow communities; as the k-blog process is based around commodity software, these groups can use the basic environment from the first day of the project to generate and share content. As the project progresses, so will the process, the software to support it and the documentation to explain it; at all stages, the k-blog process fulfils a clear and immediate need. While we are specifically targeting these communities, the k-blog process and platform is sufficiently generic that it can support a wide range of research activities.

Although presented here as a single platform, the process and components are separable and can benefit communities independently. In particular, the tools and documentation from WP2 and WP4 will find use within the research blogging community, who find, in particular, the lack of tooling for referencing difficult. Finally, the statement of a peer-review process, and its implementation within RT will be applicable to any peer-review environment regardless of the form of publication. This includes publications published using wiki or other Content Management Systems.

5.6 Engagement with Community

²⁹We consider the mechanism for engagement with four kinds of community: engagement with our core content generating community is an intrinsic part of this proposal, as described in WP3. Further interaction with more disparate groups will be maintained through personal contacts; each of the five individuals named in this proposal are experienced and embedded in different communities (health care, microarray, ontology, proteomics). Engagement with our core content consuming community is, again, an intrinsic part of the proposal; all project communications will be via open mailing list or k-blog. Project members are active users of Web 2.0 social technologies; our initial trials as part of Ontogenesis showing this approach to be highly effective form of dissemination, with minimal effort. Engagement with software users will be via website and direct interaction. All software will be released or advertised via normal channels (website, versioning, and mailing list), including a (debian) package repository for those wishing to set up their own server. Finally, developer communities will not be specifically targeted, but our open source, continually integrated development plan will be attractive, and we will accept suitably licensed contributions.

³⁰All communities will benefit from the open and agile development methodology we will adopt; changes to the environment will be integrated and released rapidly, ensuring continual improvement and facilitating rapid feedback cycles.

6. Previous Experience and Project Team

³¹Dr. Phillip Lord is a Lecturer of Computing Science at Newcastle University. He has a PhD in yeast genetics from University of Edinburgh, after which he moved into bioinformatics. He is well known for his work on ontologies in biology, as well as his contributions to eScience beginning with his role as a RA on the myGrid project. Since his move to Newcastle, he has been an investigator on there more eScience projects; CARMEN, ONDEX and InstantSOAP, as well as maintaining an active engagement in standards development (OBI, MIGS, MIBBI), and publishing on the fundamentals of ontology design. He was an active participant in the Ontogenesis network, and developed the initial idea for knowledge blogs as part of this. He is an active blogger and developer.

³²Dr. Georgina Moulton is an Education and Development Fellow at The University of Manchester. Since 2005 her main roles have been to co-ordinate the development, and delivery of multi-disciplinary bio/health informatics education programmes; and to facilitate the engagement of biological and health communities in a variety of bio and health informatics research projects (e.g., ONDEX, Obesity e-Lab). For 3 years, Georgina was the EPSRC funded Ontogenesis Network Manager, in which she co-ordinated the activities of the network and expanded the network through the facilitation of the development of new activities and was involved in the trial k-blog process. More recently her work includes the development and delivery in conjunction with NHS partners of an education and development programme tailored to match the needs of North West public health analysts and the wider healthcare workforce.

³³Dr. Daniel Swan has a PhD in developmental biology and continued to work in developmental biology as a post-doctoral researcher before moving into bioinformatics in 2001.  Subsequent positions included working for Bart’s and the London Genome Centre and the Centre for Hydrology and Ecology in informatics driven roles dealing with large, distributed biological datasets generated by large user communities.  Currently the manager of the Newcastle University Bioinformatics Support Unit, he leads a small team aiding biological researchers generate, capture, store and analyse their digital data.  His interdisciplinary background means he has grounding in both computer and biological sciences and is comfortable working on CS focused projects (CARMEN, InstantSOAP, Bio-Linux) as well as acting in a research capacity analysing high-throughput data.

³⁴Dr. Simon Cockell has a PhD in Genetics from Leicester University, and refocussed into Bioinformatics with a Masters degree from Leeds in 2005. From there he moved to Newcastle, and the Bioinformatics Support Unit. Since coming to Newcastle, Simon has worked on a range of projects involving large scale analyses (AptaMEMS-ID), data integration (Ondex) and health informatics (MRC Mitochondrial Disease Cohort). 

³⁵Dr Robert Stevens is a senior lecturer in Bioinformatics in the Bio and Health Informatics group at the University of Manchester. His main areas of research are in the development and use of semantics within the life sciences. This is blended with the use of e-Science platforms to gather and manage the data and knowledge of the life sciences. He was PI on the Ontogenesis network that ran the meetings for the first k-blog. He is or has been a co-investigator on the myGrid and myExperiment grants that will provide both content and technical input to this project. As well as the JISC funded myExperiment project, Stevens was an investigator on the JISC funded CO-ODE project that developed Protégé 4. On the back of this, Stevens has led the OWL training activities at Manchester that has directly fed in to the Ontogenesis k-blog. This range of experience makes Stevens an ideal partner to lead the development of content within this project.

The [PDF] is available here.

Abstract

Background: Many areas of biology are open to mathematical and computational modelling. The application of discrete, logical formalisms defines the field of biomedical ontologies. Ontologies have been put to many uses in bioinformatics. The most widespread is for description of entities about which data have been collected, allowing integration and analysis across multiple resources. There are now over 60 ontologies in active use, increasingly developed as large, international collaborations.

There are, however, many opinions on how ontologies should be authored; that is, what is appropriate for representation. Recently, a common opinion has been the “realist” approach that places restrictions upon the style of modelling considered to be appropriate.

Methodology/Principle Findings: Here, we use a number of case studies for describing the results of biological experiments. We investigate the ways in which these could be represented using both realist and non-realist approaches; we consider the limitations and advantages of each of these models.

Conclusions/Significance: From our analysis, we conclude that while realist principles may enable straight-forward modelling for some topics, there are crucial aspects of science and the phenomena it studies that do not fit into this approach; realism appears to be over-simplistic which, perversely, results in overly complex ontological models. We suggest that it is impossible to avoid compromise in modelling ontology; a clearer understanding of these compromises will better enable appropriate modelling, fulfilling the many needs for discrete mathematical models within computational biology.

Introduction

Ontologies are now widely used for describing and enhancing biological resources and biological data, largely following on from the success of the Gene Ontology [1]. Ontologies have been used for many purposes, from schema integration to value reconcilliation to query interfaces [2]. Ontologies have also become a cornerstone of computational biology and bioinformatics. As computationally amenable artifacts they are, themselves, a direct part of computational biology; many computational biologists are involved in their production and maintenance. Many more use ontologies to summarise their data, often by looking for over-representation [3], as the basis for drawing computational inferences about data [4], or as the basis for determining semantic similarity [5]. Even those not making direct computational use of ontologies are likely to come into contact with them, for example, when preparing annotation as part of their data release [6].

It is, therefore, of vital interest to computational biologists that ontologies for use within biomedicine are fit for purpose. One effort that aims to increase the quality of the ontologies available within biomedicine is the “OBO Foundry” [7]. The main tool that it uses for this is “an evolving set of shared principles governing ontology development”. The initial eleven principles of the OBO Foundry [8] were largely concerned with what might be termed ‘good engineering practice’ (ontologies must, for example, be openly available, with a common syntax, well documented, and used). These principles have later been joined by a further eleven [9]; these include principles such as “textual definitions will use the genus-species form”, “Use of Basic Formal Ontology” and, the somewhat quixotic, “terms […] should correspond to instances in reality”. These stem not from engineering practice, but from a perspective called realism.

The many different uses for ontologies that we have described are reflected in different understandings and methodologies about how and what to represent in an ontology. Over the last few years, for many uses the paradigm has moved from “a conceptualization of the application domain” toward “a description of the key entities in reality”; it is this latter approach that defines realism [10]. This approach to ontology is typified by the Basic Formal Ontology (BFO); a small upper-ontology for use within science in general and biomedical ontology building in particular [11].

There has been significant discussion regarding the possibility of representing only “real entities” in computational ontologies [12]. Likewise, there has been significant discussion about the philosophy surrounding realism and the role of ontology in its representation [10]. While it is argued by some that it is possible to represent only reality when making a domain description, there has, however, been little discussion on whether it is necessarily desirable to do so.

In this paper, we consider the implications that realism has for the choices that are open to the ontologist while they are modelling their domain of interest. In particular, we consider the implications that this has for the computational capabilities of any resultant ontology, in terms of its ability to represent scientific knowledge in a computationally amenable form, as well as the ability to perform automated inference or statistics over this knowledge. We suggest that the application of realism results in ontologies that are over-complex, awkward or limited; as such, realism falls far short of its aim of increasing the fitness-for-purpose of ontologies. This approach, therefore, is unlikely to fulfil the needs of computational biologists whom form a substantial part of both the user and developer community for bio-ontologies.

Methods

In this paper, we take the approach of a number of worked exemplars; this is a complementary approach to an in-depth consideration of the modelling decisions for a particular area or particular ontology, which we have used previously [13], as it allows broader conclusions about the general principles of ontology development. For each section, as well as the main exemplars, a number of related examples are briefly discussed, to reinforce that the issues raised are, indeed, general.

The exemplars have been selected by several criteria. First, all the main exemplars are all taken from within biomedicine; this is also true for the majority of the related examples. Second, we have chosen exemplars that provide as wide a coverage of biology as possible. For practical reasons, third, we have chosen exemplars where the underlying science is relatively basic to much of biology and is likely to be immediately clear to the reader without significant explanation.

We have chosen exemplars requiring as little knowledge of specific ontologies as possible. We refer to only three. The first is BFO (see “sec:what-realism-2”) which is a canonical example of a realist ontology. BFO is described as a cross-domain, upper-ontology; as a result, most terms fail the criteria given above; they are of poor biomedical relevance, and are not basic science or immediately clear. We have, therefore, also used PATO (see http://obofoundry.org/wiki/index.php/PATO:Main_Page); this defines “qualities” that we might consider attributes of other entities; so, the authors of this paper have a height, weight and shape, all of which are considered to be qualities of the authors. Finally, we use the relationship ontology [14]; this describes the relations between entities. So, for example, the height of the author inheres_in the author.

As discussed in this and other works [15, 16], “realism” is itself poorly defined. Where this lack of definition makes the consequences of realism hard to determine, we have taken the practical course, of showing the consequences as they play out in practice; to an extent, therefore, these three ontologies are not only exemplars for realism, but define it, as it is currently practiced. In short, for this paper, when we say “realism”, we largely mean “realism as practiced by BFO”. We do not claim, in this paper, to address all the philosophical perspectives that through time carried the name “realism”.

Results

What is Realism?

Building ontologies based on reality is obviously appealing to most scientists; after all the study of reality to determine its behaviour and laws is the goal of scientists. A brief consideration, however, shows that this notion cannot define a methodology for the building of ontologies.

Within the context of science “reality” would normally be taken to mean our experimental or observational data; but the statement that science (ontologies) should be based on experimental or observational data is a truism and, as such, has no explanatory power. The “real” in realism refers, in fact, to the belief that the categories that we can use to divide entities are, themselves, real.

This distinction stems from an old argument from philosophy; realism against conceptualism. Again, both sides of the argument agree that the world we can percieve, and as scientists, experiment on, is mind-independent. The conceptualist, however, argues that the categories that they term concepts are a product of social agreement. Conversely, the realist argues that these categories that they term universals are themselves real, that is mind independent in their own right, like the entities they describe.

This distinction may seem fairly confusing; as Russell [15] says “if I have failed to make Aristotle’s theory of universals clear, that is (I maintain) because it is not clear”. In fact, there is a third possibility that is a more empirical view—that is, if categories (or other models) help in describing and predicting experimental data, then they are useful regardless of whether they are real or otherwise [17]. As an example, the Mendelian notion of segregating units of inheritance was defined and useful many years before a complete mechanistic description of their cause was available. In this context, we note that there is no commonly used term to express this form of category; most commonly, “concept” is used.

For a field with a core activity of providing definitions, there is surprisingly little agreement on the meaning of the word “ontology”; as there have been many papers on the topic, we consider just a few that reflect the distinction between these approaches. Probably the most commonly cited definition [18] describes an ontology as “a specification of a conceptualization”. This definition emphasises the formality (i.e. logical and, therefore, computationally amenable) aspect to ontology development.

This is countered with a realist definition; while the requirements from Gruber’s definition—a formal specification—are necessary, realist ontologies add the requirement that “the nodes and edges correspond not to concepts but, rather, to entities in reality” [19].

What does“reality” in this context actually mean? Definitions such as “that which exists” are strangely circular leaving the question of what “exists” means. Smith [12] adds the priviso that reality is “captured in scientific laws”. Being a scientific law is not strictly enough, as some are later shown to be wrong, but a scientific law is the current best attempt at reality; this possibility does not make an ontology non-realist. For a realist ontology, the nodes are “universals”—entities in reality—rather than concepts; at least one particular must exist for every universal.

This still leaves the difficulty of applying the realist definition in practice. So most scientists will happily accept, for example, that a cell is real as it is an entity that can be observed, interacted with and manipulated. However, concepts such as “function” [13] have raised more discussion [20]; is this “real” or just a word biologists use as a point of reference? While the definition involving “entities in reality” maybe of philosophical interest, they are hard to turn into a specific assay; how to test whether a particular concept is, also, a universal. Instead of a clear assay for existence, realism offers direction about what concepts are NOT reality, rather than those that are reality. For example, and perhaps ironically given the negative practical definition of reality, a statement such as:

  Dog is_a not Cat

is not held to be a statement about reality as it is a logically constructed example of subsumption (an is_a relationship); there is no real universal containing particular not Cats in existence. Likewise,

  Dog is_a (Dog or Cat)

as the existence of particular Dogs and Cats does not mean that there are any particular Dog or Cats (examples modified from [12]).

This is not meant to provide a complete introduction to “realism”, but to provide a grounding for the discussion that follows; we will consider the issues raised by realism, throughout the paper. A more philosophical treatment of realism is given by Merrill [16]. It is useful to note that Gruber’s [18] statement that “And it [a computational ontology] is certainly a different sense of the word than its use in philosophy.”. In this paper, we are concerned with the ontologies as computational artefacts.

To summarise, a realist approach to ontology says that the categories or universals in to which objects or particulars fall have an existence in their own right. It is these universals and only these universals that a realist approach says should be the nodes within an ontology. In this paper we examine whether this approach is an adequate means to provide an account for the data produced by biomedicine.

Models that represent reality

In this section, we suggest that many universals have a range of representations. In some cases, the choice of representation may be obvious, such as length which has a natural scientific representation in SI units. In many cases, however, there is no clear set of criteria for choosing between representations. We consider the way that one quality, colour, could be represented ontologically.

Colour is a complex phenomenon. The colour of an object or other phenomena arises, in part, from that object and, in part, from the eye that perceives it.

A representation of the physical reality would be an account of the reflection, transmission and perception of light by an organism. Such an account of the reality of light and its perception might cover the following facts: Chlorophyll is green in reflection and red in transmission; a flower petal appears white to a human, but has UV stripes to a bee; the plant leaf and the algae appear green to humans, but have different reflection spectra because their chlorophyll co-ordinate to their Mg²⁺ ion in different ways.

There have been a number of different attempts to represent the complexities of colour numerically, for a number of different purposes. These are models that allow us to describe colour, without having to deal with the underlying physics or reality of colour. Probably the best known of these are RGB (Red, Green, Blue) or HSV (Hue, Saturation, Value), both of which are additive colour models appropriate for describing colour on a display screen. CYMK (Cyan, Yellow, Magenta and Black) is a subtractive colour model and commonly used for printing.

Collectively these representation schemes are known as colour models. That none of these schemes has become predominant reflects both their different uses and the preferences of different user groups.

For the ontology builder, this leaves us with a difficult choice:

1. We bless one of the colour models, substituting the model for the underlying physics and do not describe the others.

2. We describe all of the colour models, but do not describe that they are part of a colour model.

3. We explicitly describe the reality of the physics, biology and the relationship to the different colour models, reflecting the practise of describing colour in much of science.

Currently, considering the PATO ontology, which is documented as being built according to realist principles, the first approach has been taken, using the HSV scheme. So, PATO has a term Color Hue (PATO:15) that is defined as :

“A chromatic scalar-circular quality inhering in an object that manifests in an observer by virtue of the dominant wavelength of the visible light; may be subject to fiat divisions, typically into 7 or 8 spectra.”

Using this model, PATO describes red (PATO:322) as :

“A color hue with high wavelength of the long-wave end of the visible spectrum, evoked in the human observer by radiant energy with wavelengths of approximately 630 to 750 nanometers.”

This modelling approach has a number of limitations.

• The decision to choose one colour model or the other is arbitrary. While there are reasonable justifications for the use of HSV as opposed to, for example, RGB, there is no a priori justification for use of an additive colour model as opposed to a subtractive model. Both are valid, for different usage; in general, reflective colour is more common in biology (e.g. pigmentation) than emitted colour (e.g. fluorescence) which would suggest that subtractive models are more generally applicable, but a full treatment requires both.

• There are no terms which can be used to express data described according to other colour models, necessitating a transformation between the different models into the officially “blessed” version during application of the ontology. These transformations may be lossy and not fully reversible.

The second approach is also possible. This would allow expression of data in multiple colour models, however:

• The ontology would tend to get rather confusing as more colour models are added; colour would have children “Hue”, “Red” and “Cyan” and seven other sibling terms.

• It is not clear which terms comprise a colour model: do values for “Hue”, “Green” and “Magenta” specify a colour?

• It is not clear whether terms that occur in the other contexts are equivalent. Is “Red as in RGB” the same or different as Red (PATO:322)? Is “Hue as in HSV” the same or different from “Hue as in HSL” (HSL is another additive colour model).

The third approach does not suffer from the limitations described. We suggest from this analysis that it is necessary, if unfortunate, for some qualities to be explicitly described with multiple representations. To avoid confusion, the universal quality, colour, would need to be explicitly described as having multiple valid models. Yet, realism argues that we should not do this, as colour is real and not a model; more over, the focus on realism means that the documentation does not describe the choices that have been made, nor refer to the relationship between Color Hue (PATO:15) and “Hue as in HSV”. In short, realism has limited our ability to represent colour.

Related Examples

There are many different examples of this issue; having two or more models to describe the same part of reality is common. The distance between two markers on a chromosome can be measured using (one of a number of) genetic techniques. Some qualities have a bewildering array of different measurements associated with them; Wikipedia, for example, lists 13 different measurements of concentration such as molarity or \(gm^{-3}\).

This issue has been previously recognised. In computing science, explicitly modelling one model in another is a form of metamodelling. Other, non-realist, upper-ontologies such as DOLCE use the concept of Quale to describe a cognitive abstraction (such as Colour), including those over a physical quality (such as the spectral properties of reflected light) [21].

Sequences and the Central Dogma

The central dogma of molecular biology suggests that all genetic information is encoded in the DNA of a cell, as the ordered nucleotides that comprise the DNA. RNA is transcribed from this DNA. The RNA molecule also has a defined order of nucleotides related to the DNA. Finally the RNA is translated into protein.

Consider an ontology describing these entities. First, the DNA molecule has a number of properties; as well as physical dimensions (discussed further in “sec:limits-consistency”), including a length expressed in metres, it consists of a number of monomeric units. So, for example, we might say a DNA molecule with a series of nucleotide residues represented as ‘GATC’ has­Monomeric­Part 4.

This causes a slight worry from a realist perspective; the number 4 may not be a realist universal. There are no instances of 4. In this case, the number 4 is being used to describe a part of reality, so this is allowable in a realist ontology. Alternatively, we could describe the same reality using units (traditionally base-pairs or bp). Therefore, the DNA molecule has­Polymer­Length 4bp.

Accepting the use of natural numbers in this way, also means that we accept the use of sets and sequences to describe reality. One definition of 4 is a sequence. Stating that the DNA molecule represented with the sequence ‘GATC’ has­Polymer­Length 4bp is equivalent, therefore, to stating that it hasSequence ‘NNNN’ where ‘N’ is any nucleotide residue.

It should be noted, however, that the usefulness of these statements stems from our implicit knowledge. The number 4 is a natural number, so has­Monomeric­Part 4.2 is not possible. If a new monomer is attached to our DNA molecule, it will now has­Monomeric­Part 5, because the natural numbers are additive. We understand the operation of natural numbers as part of our shared, background knowledge, and we can apply this knowledge here.

Having described that the DNA molecule represented as ‘GATC’ has­Polymer­Length 4 (or hasSequence ‘NNNN’) we might wish to be more specific about the order of nucleotide residues and state hasSequence ‘GATC’. The implicit background knowledge we used previously about the natural numbers still applies here.

Next consider the process of transcription. The previous discussion about DNA likewise applies to RNA. The RNA molecule will, however, hasSequence ‘GAUL’, as RNA uses a different set of bases to DNA. Mathematically, one sequence can be determined from the other by applying a mapping; though the mapping is a human activity, not a representation of biochemical reality. To describe this, we have two options:

• Taking the realist approach, we can continue to rely on the implicit knowledge of the biologist, as we have previously relied on an implicit understanding of the natural numbers.

• We can be explicit about the properties of these sequences (additional to those properties shared with the naturals). We can talk about non-real world concepts such as alphabets, transformations and how these map to the real entities involved.

It should be noted that the former severely limits the ability to describe the central dogma. The transformation of DNA to RNA sequence is simple, but the transformation of RNA to protein is more complex. Again, the choice is between representing reality or representing how we practise science.

Related examples

The issues relating to sequences are fairly general. In computer science terms, these are abstract data types. The DNA sequence is a kind of sequence with special properties (a limited alphabet). Many of the physical quantities in science have special properties in this way. Consider:

Temperature:

While these look like positive real numbers, temperatures are only meaningfully subtracting from each other, which gives information about heat-flow between two bodies. Other operations (addition, multiplication) which are useful for real numbers have little meaning for temperature.

Recombination Distance:

These look like probabilities but are not, requiring a transformation to add.

There is a limitation on the ability to use abstract data types within a given ontology language; in most cases, the expressivity of the language will not allow arbitrary mathematical relations. Some languages, such as OWL, for example, provide “concrete domains”; these provide extension points within the ontology language where, for example, the special properties of temperature could be represented; other languages do not. In either case, there are limitations to these capabilities; for example, the constraint and behaviour of a concrete domain needs to be interpreted with its own semantics within a reasoner, rather than expressed explicitly within the ontology. It may make more sense in many circumstances to describe the existence of a mathematical model as discussed in “sec:go-where-science”.

The limitations of computers

Modelling continuous properties is a common problem in ontological engineering. For example, according to statistics the western world is now facing an obesity epidemic; in short many or most of us weigh too much. Understanding, however, exactly what “too much” means is not necessarily simple; a common technique to use is body mass index (BMI)—body weight divided by square of the height, which is a continuous value. The BMI range is split into 4 categories: Obese (>30), Overweight (>25), Normal (>18.5) and Underweight (<18.5). These categories represent ranges of the value of BMI.

This data simplification has many justifications. On an individual basis, the BMI is not a particularly accurate measure, so the simplification does not lose much accuracy. It is also easier to describe to patients, for whom a “BMI of 25” will be less comprehensible than being “overweight”.

Modelling some of this is straight-forward. Height and weight are modelled as properties of the individual. The BMI would therefore appear to be a property of the individual as it is a restatement of two existing properties. It would appear, therefore, that the category into which an individual falls should also be a property of the individual.

Consider the values of the property next. These categories are an abstraction over the real-world properties. Although, height as an integer value is expressed using a non-real-world entity, it is a description of a part of reality. A range, however, in the BMI does not describe part of reality in the same sense. There are no instances of BMI “Obese”. In a realist ontology, therefore, it is unclear what the relationship is between BMI Obese and the individual person.

For the statistician or computer scientist, there is an additional advantage to the simplification; four discrete groups have better computational properties than a continuous measure. Database queries become easier to write, and quicker to run. This is also true for the ontology builder; simplifying the real-world may fulfil the needs of an application for which the ontology is built, while avoiding unnecessary complexity. This is a widely used method for representing partitions of continuous values, the appropriately named value partition [22].

In the case of BMI there is a pre-existing social agreement toward a set of categories; however, even in the absence of such an agreement, the ontology builder might wish to represent a continuous range as a value partition to decrease the complexity of their ontology. The value partition is useful, but many of the concepts involved are not realist universals. The choice, then, is modelling “reality” and modelling a simplification that is easier to use and has better computational properties.

Related Examples

Splitting the two cases, there are many examples of pre-existing simplifications. From medicine, there are so many that it seems to be the norm rather than the exception: hypo- vs hyperthermic; hypo vs hypertensive; hypo- vs hyperglycemic. In many cases, these ranges have standard interpretations akin to the BMI.

There are likewise a number of constructions or design patterns that reduce complexity, extend the effective capabilities of the language or simply provide standard solutions to common problems [23].

To go where science has gone before

Many experiments in biomedicine require the measurement of some physical property of a biological system. Take, for example, the measurement of heart rate; in standard practice, this is measured in beats per minute, and is calculated simply by counting beats (\(b\)) over a time period (\(t\)) and dividing one by the other (\(b/t\)). However, what time period is appropriate? We might choose 60s, but this raises the question, what is the meaning of heart rate over shorter periods?

Fortunately, there is a standard solution to this problem, which is to define heart rate using differential calculus; so heart rate becomes \(db/dt\).

The derivative, \(db/dt\), presents some problems from a realist perspective. As noted previously (see “sec:sequ-centr-dogma”), it is possible to associate real numbers with entities; however, \(db/dt\) is \(0/0\). It is not clear whether this quantity is a universal; it is certainly the case that the expression \(db/dt\) is not a universal, yet such values and calculus itself is apowerful tool within science and not using it within ontological models is a severe restriction.

We can describe this ontologically in three ways:

• We can model the real world entities involved – beats, time and describe nothing else.

• We can describe rate in mathematical terms. In this case, we are defining the heart rate as a mathematical abstraction.

• We can model the heart rate as a real world entity, \(db/dt\) as a mathematical entity and explicitly state that $latex db/dt is a model of heart rate.

These different solutions present different advantages. The first is consistent with realism. The second is consistent with the most common definition used within science. The third is consistent with both but it is unclear when to use which term (for example, is \(\Delta {}b/\Delta{} t\) an approximation of \(db/dt\), a quantification of the real world quality or both)?

In most cases for the description of science, the second option makes most sense; conflating the mathematical model with the real entity enables us to use the advantages of two different modelling techniques without introducing the confusion of the third option.

Related Examples

There are many related examples from mechanics, electromagnetics or chemistry; as with value partitions in medicine, so many that they appear to be the norm. All of these subject areas have direct relevance to biology and, perhaps even more so, to the equipment used in the practice of biology.

Mechanical examples would include velocity (\(dr/dt\)) and acceleration (\(d^2r/dt^2\)). Electromagnetics would include current (\(dC/dt\)) and capacitance (\(dV/dt\)). Chemistry examples would include rate constants and pH. In biology, population biology, systems biology and neurosciences make wide use of mathematical models. The lack of a link in realist ontologies to these mathematical models is not free from consequences (described further in “sec:discussion”).

The more general issue comes not from relating to differential calculus, but relating to pre-existing non-ontological techniques. For example, taxonomy in the linnean sense. There have been many discussions about whether species and high taxons are reflective of reality; it is certainly the case that a number of higher taxons do not reflect phylogeny [24]. Given that it is of uncertain status, should we represent taxonomy as a quality of an organism, an independent conceptualisation of the biologists or both?

The limits of consistency

Physical biological entities such as cells and organisms have an extent in the real world. This paper’s first author, for example, has a height of around 1.8m; a similar value cannot be applied meaningfully to the electronic version of this document, although it may apply to the paper that it may be printed on.

There are a number of different, well-understood mechanisms for representing physical space. We can use a dimensional or cartesian model, with three perpendicular lines with a linear scale. We can use a polar model, expressing extent using angles and a single distance. Modern physics has told us, however, that all of these are limited models of reality; physics generally uses a four dimensional Minkowskian spacetime model; here the axes are not linear; motion of the observer down one will change values down the others. Alternatively, at a quantum level, length is a probability distribution.

For the ontology builder, this leaves a difficult choice and the same choice discussed previously in “sec:colo-colo-models”: Represent the reality physicists relate; bless one, ignore the rest; describe their components but not their models; explicitly describe them.

If the ontology builder is to be consistent, then, they should make the same choice in both cases; if we describe colour models, we should explicitly describe Minkowskian spacetime, quantuum probability distributions, cartesian and polar systems.

There are, however, two important differences to colour models. First, there is a strong social bias toward cartesian systems. Secondly, within the scope of biology and the life sciences, four dimensional spacetime or quantuum models confuse rather than simplify; the relativistic corrections produce such small differences that they are statistically meaningless; similarly, describing a leg as a probability distribution adds little other than complexity.

This leaves the ontology builder with two options:

1. We can build an ontology with a consistent relationship to reality. So, having decided to explicitly represent colour models, this suggests that we should also explicitly model 3D space, 4D spacetime and the various co-ordinate systems that are used to describe these.

2. We build an ontology with an inconsistent relationship to reality. So, we might be explicit about colour models, but arbitrarily bless 3 dimensional space, using cartesian co-ordinates.

The compromise here is very straight-forward. The first solution retains its consistency to reality, the second is consistent with usability and usage; for biomedicine, a 3D cartesian co-ordinate system plus time is likely to be enough for the foreseeable future and makes life easier in the meantime.

The Newtonian view of the world is the best model in this case: it is good enough. When building an ontology for biomedicine, it makes most sense to use this view as it will produce the results required. If, in the future, biomedicine advances so that relativistic or quantuum representations are necessary, then current ontologies will need refactoring; even then, this future cost is likely to be offset by gains in the present.

Related examples

In the choice of units for measurement for scientific purposes, SI units are to be preferred. It should be noted, here, that there is a domain dependency; for an engineering ontology, the use of American imperial units would be inevitable.

For most of biology it is unnecessary to distinguish between the length of the calendar year and the astronomical year—the latter changing with respect to variability in the motion of the earth. There are occasions when this distinction may be important for data integration in bioinformatics as leap years and leap seconds show.

For an ecologist counting the number of trees in a sampling square 100m by 100m, they will take the area as 10,000m²; The surface is, however, neither smooth nor a Euclidean plane, so this area is wrong in reality. For much of ecology, this distinction will not matter. Again, there is a domain dependency here; whale or bird biologists interested in migration patterns may well care about the curvature of the earth.

Discussion

Realism has been held up as a methodology for “good” ontological modelling, and the production of more tightly defined and consistent ontologies. In this paper, we have discussed five different cases, with biological examples, that we might wish to model ontologically; for each, we have presented different models, describing the same underlying science. In each case, a realist solution is possible, but places either limitations or awkwardness on the models produced.

Building an ontology with a consistent relationship to reality may help to enable interoperability [7] under some circumstances. If, however, it disallows modifications for computability (see “sec:work-around-comp”), or requires arbitrary blessing for one form of specification over another (see “sec:colo-colo-models”) it may have the opposite effect.

Nor are the issues discussed in this paper free from consequences. In “sec:go-where-science”, we discussed interoperability with existing scientific models. Mathematics and physics have produced complex, refined and expressive notation systems, representing a deep understanding of how numbers and the physical world work. These are, however, not being used in current ontologies and this results in a lack of precision, errors and omissions:

Lack of Precision:

The PATO term speed (PATO:8) which is defined as:

“A physical quality inhering in a bearer by virtue of the bearer’s rate of change of position”

with a synonym of velocity; from this definition, we cannot distinguish the vector and scalar quantities of velocity and speed; indeed, it is not clear which of these two speed (PATO:8) is. Meanwhile acceleration (PATO:1028) is defined as:

“… the rate of change of the bearer’s velocity in either speed or direction”

which is implicitly a vector quantity, and contradicts the statement that speed and velocity are synonyms. The mathematical definitions (velocity as \(dr/dt\), speed \(\left|{dr/dt}\right|\), acceleration \(d^2r/dt^2\)) are precise, concise and accurate.

Errors:

Similarly, length (PATO:122) is defined as a quality; qualities have to inhere in Independent Continuants; as a Spatial Region is a child of Continuant this means that Spatial Regions cannot bear lengths. In short, in current versions of BFO, there is no intuitive way of modelling the length of a region in space.

Omissions:

BFO is mass-centric; it is currently unclear where many physical entities exist, examples including energy, waves (through a medium) or EM radiation. Likewise, it lacks a natural position for numbers (that have no particulars), patterns and distributions. Yet, these entities are key to a physical description of the world.

To our mind, these are indicative of some of the most serious flaws of realism-based ontology building. It makes little sense to replicate the models of physics using English instead of a more precise mathematical notation. If BFO had been built using direct links to a grounded physical model of the world, it seems likely that these problems would not have arisen.

We have discussed a number of concrete examples where building an ontology by considering realist concerns has detrimental consequences for the model. We believe that the real world entities and the relationships between them is only one consideration among many: simplicity, usability, fitness for purpose are equally important.

Taken to its most extreme form realism, it seems to these authors, would produce models unsuitable for use within science. There is a choice between a correct account of reality that does not allow the data of science to be adequately described and a description of reality that takes in to account how science is performed. Fortunately, most “realist” ontologies are not really so: PATOs representation of HSV for modelling colour is not a bad decision; it represents a straight-forward, pragmatic approach to ontology building, where the representation has been chosen on the basis of a use case, not the entities as they exist in reality. Similarly BFO uses a 3D plus time model of reality; it suggests that length are properties of the entity alone, without reference to the observer. This is not a true reflection of reality, but one which is a good enough approximation for use within the biomedical sciences; in short, usability and simplicity have been considered to be more important in the modelling process than the relationship of the model to reality. In accepting these compromises, BFO has placed itself squarely as a computational rather than philosophical ontology.

Despite these concerns, realism has made a contribution to the field of biomedical ontology engineering. By emphasising the importance of real-world entities and by encouraging a more specific interpretation than the generalisation of a “conceptualisation”, realism helps to avoid the introduction of unnecessary layers of abstraction. A consideration of the entities in reality may be a part of an ontology engineering process; ontology builders should have careful and considered reasons for diverting from modelling in this way and that ontologies should explicitly describe through annotations the terms that do or may divert from this view. Ontology builders should, however, be free to make this decision; the acceptance of compromise with respect to reality will result in simpler and more effective knowledge artefacts.

Johansson [10] when discussing realism asks the rhetorical question: “would you like to be treated for a physiological illness by a (non-realist) physician who is not sure that there are human bodies?” – (our emphasis). As scientists, our reply would be if their survival and success statistics were the best, we would not care whether they were a realist, a non-realist or a robot which admitted of no philosophical position at all; also, using a doctor who was strictly realist and thus cut off from much of the practise of science (such as determining heart rate) would disturb many patients. As bioinformaticians, we build ontologies to provide a descriptive and predictive model of the wealth of experimental data that is now available. In biology, the job of an ontologist is to describe data such that it can be analysed. Naturally this entails a description of entities in reality; it also, however, entails a description of science, and it entails compromise; we overlook this to our peril. The last 200 years of science shows the success and strength of this position; it is on this groundwork that we should build for the future.

And, secondly, I got notification from the British Library that they will be archiving the website. Good news, although there are not archives available yet.

We move forward!

The bid, in this case, was for extensions to the Knowledgeblog environment; we want to make sure that it supports research better than at the current time. Our initial experiences were generally good, with a few naysayers. Additionally, we wanted much better linking to external forms of data; array express, Swissprot and the like. And, finally, we wanted to trial this out against a set of specific use cases. Critically, I also got tired of writing “knowledgeblog” the entire time, so they will now be “k-blogs”.

If it gets accepted, we proposing to develop some additional functionality, often reusing existing software. We really are trying to avoid developing any software that we don’t have to. The plans include:

1. A documented k-blog process, including information on who does want, and how to use various existing tools (word and latex in particular).
2. Proper support for referencing — authors should be able to drop in a PMID, or DOI and get a reference list and in-text citation automatically.
3. Various metadata support, so that the in-text citations have semantics from the readers side.
4. Trackback proxying for those resources which don’t support trackbacks.
5. Integration and additional tooling for adding references and cross-links.

I’m hoping that we get the money; if we do, the work will give us a platform on which to build a publishing environment, a place for an educational resource, and finally, and excellent extension point for playing with semantic forms of publishing. I am not sure what the odds are; I know quite a few other proposals are going in, and there’s a reasonable chance that George Osbourne will cut the money back before its awarded. All I can do now is wait.

I’ll probably blog the whole proposal in a few days; this gives me a chance to try out the “blogging from Word” experience. How exciting.

This is quite exciting, as we did the original work quite a few years ago, and it looked very promising; despite the gap, I still think this could work really well. Since that time, system biology has gained currency; this work fits, as we aim to look at the mitochondria as a whole. Instead of an in depth mathematical model of part of the mitochondria, as is common in systems biology, we will have a light-weight logical model of both what we know about the mitochondria and how we know it.

Please feel free to distribute this!

PhD Studentship, 2010

EPSRC PhD Studentship Building a logical model of biology: the Ontology of Mitochondria

For this project, you will use cutting edge technology designed for the Semantic Web, and apply it to the new field of systems biology. Specifically, you will develop an OWL ontology, a formal, logically specified model, to describe the mitochondria, a subsystem of the cell. You will use this to integrate large amounts of real-world data, to search for inconsistencies and produce a predictions about the underlying biology. From a computing perspective, this will result in insights both about the technology, and its scalablity; from a systems biology perspective, you gain understanding of the value of models which are wider than traditional mathematical models; from a biomedical perspective, you may gain insight in the functioning and behaviour of a medically important system of the cell.

This is a challenging multi-disciplinary project; applicants are not expected to understand all its aspects at the outset; as a result, it is of interest to those from either a computing science, computational biology or bioinformatics background. Any experience of ontologies, modelling or mitochondrial biology will be an advantage, but is not required. A willingness to learn is critical; students will spend significant time in both a computing science and biology environment, and will become familiar with both.

You should have either a First or 2.1 in Computing Science, a Biological Science or Mathematics, and a distinction level Masters degree in a related subject. Equivalent experience will also be considered.

Depending on how you meet the EPSRC’s eligibility criteria, you may be entitled to a full or a partial award. A full award covers tuition fees at the UK/EU rate and an annual stipend of £13,290 (2009/10). A partial award covers fees at the UK/EU rate only.

For further details, please contact Phillip Lord <phillip.lord@newcastle.ac.uk>.

Since the first meeting, I’ve had plenty of time to reflect on the general idea of knowledgeblogging. As far as I can see, there is one overwhelming truth about the situation; we got 15 articles in 2 days and, since then, we have been averaging between 500 and 1000 page hits a month. Now, of course, it’s an open question whether this is at all sustainable; we have no advertising and no financial support. But, still, our most read article (“What is an Ontology”) has had several hundred reads and, bottom line, that is pretty good going for an academic article. We might like to think that the work that we do is important (well, it is!), but in publishing terms we are pretty much of a niche market.

On the negative side, we have had articles flooding in and none of those from the last meeting have got any further. Thinking back to Nupedia, many moons ago, it’s obvious that getting an authorship is always going to be a problem.

I’m also going to have to think of a snappier and short name for than “knowledgeblog” which is taking far too long to type. So far:

k-log

Simple, straightforward, but already used

knowblog

Good, but a homonym for “noblog” which is confusing.

knoblog

Pronounced “noh-blog” would be great, but English is not a phoentic language

knob

“KNOweledge Blog” — excellent in many ways, but I realise that the entire world does not share my slightly puerile sense of humour.

Hmmm. Comments welcome. So long as they are not about my puerile sense of humour.

I was hoping for two things out of the meeting; the first was to get some content. There has been a pressing need introductory material on ontologies for a long time now. We were never going to address this completely in a two day meeting even with the significant number of people that we had in the room. But, we managed to write quite a number of articles between us — I rather let the side-down with only one small article, but I have the excuse that I was busy answering questions. Most of these have not achieved the required number of reviews yet, although I’ve just done the second reviews for Mikel’s, so once that’s posted, we should be there for at least one article. I think that people enjoyed the process enough that some more articles will appear over time, although, inevitably, once the immediacy of being in the same room will mean that this process will not happen as rapidly.

The second question was to get a clear understanding of whether the idea of knowledgeblogging has legs; it seems reasonable in theory, but does it work in practice. There were some issues — the server crashing twice out of memory was not ideal, although quickly resolved. Quite a number of people who hadn’t blogged before found the wordpress interface, particularly the editor, fairly nasty; it’s not really designed for large posts. The review process also was a little clunky and there were many questions and ideas about this. However, for my money, the 80/20 rules comes in; we got 80 percent there with a more-or-less modified wordpress. Well, maybe, 70/30.

The rest is going to require more thinking about.

I finished fiddling with wordpress yesterday and, hopefully, all is ready (fingers crossed that our server doesn’t get hacked as happened to this blog a few days ago). I’m hoping that we manage to get a number of articles written during the meeting; in practice, getting people in one room is the best way of getting these things done. However, this is not a closed process; I’d welcome articles from anyone, as well as those not at the meeting. Being blog based, the system is inherently distributed. So, if you have an ontology-related topic that you have a burning desire to write about, please contact me and I’ll let you know whether if anyone else is doing it. Alternatively, there is a list of topics that we hope to make a start in covering. The articles will be peer-reviewed and available for the world to see, fully-credited to your name.

I can’t guarantee that it’s going to be included in the REF, but I am working on it.

One of the things that I am looking to next year is the last ontogenesis meeting. It’s been a lot of fun doing these, I’ve enjoyed them all. The last one is my idea, and I think it’s going to be good. As an ontologist, you get a lot of questions about how to build ontologies and is there a book. At the moment, there isn’t really one and it’s a problem. So, for ontogenesis, we decided to write a set of book chapters; here is the clever bit — we just stick them on a blog, because the process of formal publication as a book is long-winded, tiresome and error-prone. I’m calling the process knowledge blogging — it’s peer-reviewed, formal and with no intention of being regular; articles come when they are written.

I set up the blog sometime ago. I haven’t, as yet, had a lot of time to fiddle with theme or organisation. There is some content, but it’s just the wordpress default theme. Not ideal, and I hope I will have some time for fixing things after I get back from holidays. I’ve noticed two problems already though. First is that with longer articles you need section headings and wordpress doesn’t do them; I’ve found a solution for this, in the shape of a contents table plugin, although subsequent googling also came up with others. This should make navigation a bit better.

The other issue is references — I don’t have a good idea about how to do these sanely. I’ve been looking for DOI wordpress plugins, but can only find one from crossref which doesn’t do what I want. This allows you to search for citations; what I wanted was to put a DOI in code and have it present properly.

Still I think I know how to do this; I’ve found a tool for linking references to the Mormon books; not normally something I would download, but the principle is the same. So I can replace DOIs with a proper link, using a DOI resolver. What I’d really like to do is have a proper in-text citation also. The documentation on DOIs and metadata harvesting is all rather nasty though; a nice simple REST API would do the trick.

It all confirms my long-held concerns about DOIs; there are a tool for the publishers. Still, perhaps pubmed will come to my rescue. Next place to look.

Happy Christmas to all my subscribers of whom there are very few.

I spend a considerable time working on the paper, which will accompany the release. I got this job, mostly as to regularise and clean up the English, but in the end did rather more than this; I hope people are not upset about the stuff that I took out; the whole thing was done “pair programming style”, although I had different pairs for different sections.

Despite all the efforts, though, there are still tracker items open for the 1.0 release, and thats not ideal, but it is good that we are much closer to it.

Philly was much as I remember it; it’s a reasonably pleasant city. It doesn’t feel too aggressive and it’s relatively quiet. As I had a late flight out today (meeting finished yesterday), I spent the time wandering around town; like too many US cities Philly has been built to be easy to drive through, rather than good to live in, but you Philly is okay for walking around. They have a nice parkway area on JFK boulevard; I had a nice guided tour around the Rodin museum, which was wonderful, even if lacking The Thinker which is normally their show piece entranceway sculpture. Rodin was big on hands, it turns out, and rather fond of the musculature of backs; the captions on the bronzes suggest that he was having affairs with many of his models, so I wonder if this stems from…well, you can work it out.

After that, I wandered up to the art museum past the twee statue of Rocky Balboa, and the converse footprints sculpted in the stairs. The art museum itself is huge; the Thinker is temporarily here, so I got to see it after all, but I think it needs to be outside. As well as the traditional galleries, and strangely, they also have a lot of furniture there, and have imported whole rooms from various places. For me, the Asian section was the best; they had an Indian temple, dark and brooding in the half-light, and a Chinese room with the most amazing timbers. I felt the indoor Romanesque outside courtyard (erm…) was taking it a bit too far.

Not much left to be done after an afternoon full of culture; on the way back to the hotel, I looked for a little park on Chestnut that I had wanted to see and a falafel shop which I had seen sign posted. I found neither; the park had left no traces at all, the falafel shop I found a poster for, but I walked all the street and as far as I can tell 1740 Sansome is a multistory parking lot.

Back where I started, sitting in the airport; tick, tock, tick, tock.

I’m quite looking forward to it, in some ways. I quite like Philadelphia, at least if my memory serves me well; I’ve only been there once, for the SOFG conference many, many moons ago, certainly in my pre-blog days. I remember it as a pleasant town, with a water-front, only slightly scarred by the enormous roads that make US cities less livable than European. I’m also hoping to catch up with Robin McEntire, who was one of the co-chairs of Bio-Ontologies at ISMB, and is local.

I’m rather unprepared for the meeting. There has been a lot of activity on the mailing list recently, some of it concerned with paper preparation. But I’ve been trying to get the rest of my teaching preparation finished (nearly done now) which has left me very busy over the last few weeks; I haven’t even had time to look at the paper; I’ve hardly read even the mailing list subject lines. Still, the next week is entirely given over to OBI, which will have to be enough. Travel at this time of year messes with my life to an extent that I’m certainly not going to feel guilty about it.

The flip side of being busy, is that I am now in the process of writing about 5 papers, with the next 2 in my head. After the confusion of moving to Newcastle, working out what research to do and learning to teach, my research was getting a bit stuck; I was running out of ideas for the simple reason of not having time to think. Having an enormous backlog of nearly finished, half-finished, and hardly started good ideas (most of which will, in time, turn out not to be) for papers makes me feel like a proper academic again.

Their idea is this; if you are on too many grants that fail, then you won’t be allowed to submit again until you have been on some sort of re-education camp. The basic criteria appear to be this: three or more unfunded proposals, ranked in the bottom half, and lower than 25% success over the same two years.

The first criteria is problematic because it is based on an aggregate score; it is impossible to judge in advance whether you are going to be in bottom half; your proposal could be brilliant and internationally outstanding (EPSRC is like Lake Wobegon, all the grants are above average) and you could still be in the bottom half. The second half of the criterion is also interesting; if you submit a single proposal and it gets rejected then you are fall into this category straight away. It’s also going to mean that it’s going to be harder to get people to do collaborative grants, as it might bring their stats down. This is after EPSRC have been pushing us for years to put at least 5 different institutions on each proposal if we want it to be funded.

At the same time, information about the REF which is to follow up from the wonderous RAE is starting to trickle out. Nice to see that they are still going to reinforce the existing closed publication system with more bibliometric data. The “You are the REF” website offers itself as a way to work out your score. Excitingly the first question is “What is your discipline?”; Computer Scientist or Biologist. This seems reflective of the REF documentation that I have seen already. It works on this basis: different disciplines have different rules, so we will make different decisions in each, which is fine, because no one can be in two anyway.

Glad to see that the REF is carrying on the RAE tradition of encouraging multi-disciplinary research.

In bioinformatics, there are essentially two choices that is OWL and OBO (format). A second issue, is finding a good environment for developing the ontology; this divides between Protege, OBO-Edit and the ever-present “text editor”. It’s often the case, that we want to use both of these at the same time. Take, for example, OBI, which I am involved in. While the ontology itself is being developed in OWL, many of its dependent ontologies are built using OBO; being purist and demanding one is really not an option. OWL itself has many different syntaxes; at the moment, I generally prefer Manchester sytnax because you can edit it with text-editor, which is really not so easy with any of the XML representations.

While these two languages have somewhat different expressivity, there have been a number of descriptions of how to translate both the syntax and the semantics which have been described elsewhere. One of the recurrent problems, however, stems from the best practices and the syntax of identifiers.

OBO makes use of a numerical, semantics-free identifier and a namespace, with a syntax of NAMESPACE:IDENTIFER. So, a Gene Ontology term looks like GO:0003674. The namespace is not constrained to be two-letters and has mechanisms for world-uniqueness, in that people talk to each other and sort it out, if they clash. The use of a semantics-free identifier means that term names can be changed while maintaining the implied meaning with the term; the label for the term, meanwhile, provides a human readable version, which can be shown to users of the ontology. I will call these the OBO identifier and OBO label respectively.

Translating this, however, into OWL, including Manchester syntax causes significant problems. The naturalistic translation is to turn the OBO identifier onto the identifier in OWL; the OBO namespace would become an XML namespace, the OBO identifier would become an XML identifier. Unfortunately, this doesn’t work. First, the OBO identifier is genuniely just a short string and XML requires a URI; so a mapping between OBO identifiers and URIs is necessary. Second, the OBO identifier is numerical; unfortunately, while the identifiers in OWL can contain numbers they have to start with a non-numerical character. The standard translation, therefore, uses in most cases an OBO wide URL (http://purl.obolibrary.org/obo/), although some ontologies have their own namespace (GO uses http://purl.org/obo/owl/GO#). The OBO identifier is mapping to an valid identifer by sticking a prefix onto the numbers. So, we have identifiers such as GO:GO_0042101 or obo:OBI_1110045. There are also some OBO ontologies for which this does NOT occur; for instance, BFO classes in OBI come out with identifiers of the form snap:Continuant or span:Process, except for one which is bfo:Entity.

Again, all perfectly reasonable, but unfortunately, when converted to Manchester syntax it means that we end up with classes that look like this slightly elided class from OBI:

Class: obo:OBI_1110161

    Annotations:
        rdfs:label "T cell epitope ELISA IL-1b assay"@en,

    SubClassOf:
        obo:OBI_0000661,
        obo:OBI_0000299 some (obo:IAO_0000109
        and (obo:IAO_0000136 some obo:OBI_1110196))

which completely defeats the aim of a human-readable syntax. Now OBO format has much the same problem; relationships to other classes are specified using cross-referenes to their identifiers which are, essentially, unreadable. OBO format works around this with a denormalisation as can be seen from this somewhat elided example from IAO:

[Term]
id: IAO:0000027
name: data item
def:"a data item is an information content entity that is intended...."
is_a: IAO:0000030 ! information content entity

The cross reference in this case is a subsumption link to IAO:0000030

One solution would be to use the rdfs:label in place of the identifier. So, we would have something that looked like this:

Class: "T cell epitope ELISA IL-1b assay" @en

    Annotations:
        obo:identifier "1110161"

    SubClassOf:
        obo:OBI_0000661,
        obo:OBI_0000299 some (obo:IAO_0000109
        and (obo:IAO_0000136 some obo:OBI_1110196))

Other identifiers would also have to be changed, also. I’ve also added the odo:identifier line (which I think would be valid, but might require the creation of an OWL individual). Without this, it would not be possible to go backward.

However, this is problematic as it changes the serializiation between the OWL Manchester syntax and other syntaxes of OWL. The class identifier has to be URI legal, and OBO label here is not. We could do a syntactic conversion (e.g. T%20%cell%20%epitope) but this, again, reduces readiblity, defeating the point. Also, the rdfs:label would become part of the final identifier URI, which then becomes a semantics heavy identifier. Finally, it would require a OBO specific loading of the Manchester syntax, taking the URI identifier from the annotation block, and the rdfs:label from the class name.

So, is there any solution. First, there are tooling solutions. In Protege, it is already possible to use any component of the definition in the display. So, you can set the rdfs:label as the main display form. Tooling solutions are attractive, but there is a problem; you have to extend all tools to support this view; I realise that the number of freaks who wish to edit OWL with emacs is not that large, so this might not seem an issue. However, many people wish to develop ontologies collaboratively using version control; if you want to compare versions you use diff, so we now need an Manchester syntax diff viewer. Also, if you want to do some perl hacking, or straight-forward search and replace, again, it’s all harder.

To some extent this might seem trivial, but then the entire purpose of Manchester syntax (and the functional syntax) is to have an easy to read and manipulate syntax which the XML version of OWL is not. This purpose is defeated if it’s hard to read.

So, a second non-tooling solution. The obvious answer is to take the OBO approach and add comments. Now, the Manchester syntax includes a comment character (#), although last time I tried the Protege parser doesn’t implement this. None then less, it allows this:

Class: obo:OBI_1110161 #"T cell epitope ELISA IL-1b assay"@en

    Annotations:
        rdfs:label "T cell epitope ELISA IL-1b assay"@en,

    SubClassOf:
        obo:OBI_0000661,
        obo:OBI_0000299 some (obo:IAO_0000109
        and (obo:IAO_0000136 some obo:OBI_1110196))

This is not too bad, but it doesn’t work well for complex class expressions. I can’t be bothered to look up the labels and have reused one, but you get something like:

Class: obo:OBI_1110161 #"T cell epitope ELISA IL-1b assay"@en,

    Annotations:
        rdfs:label "T cell epitope ELISA IL-1b assay"@en,

    SubClassOf:
        obo:OBI_0000661, #"T cell epitope ELISA IL-1b assay"@en
        obo:OBI_0000299 #"T cell epitope ELISA IL-1b assay"@en
        some (obo:IAO_0000109 #"T cell epitope ELISA IL-1b assay"@en
        and (obo:IAO_0000136 #"T cell epitope ELISA IL-1b assay"@en
             some obo:OBI_11101 #"T cell epitope ELISA IL-1b assay"@en
             ))

This has three problems. Firstly, we have used comments “meaningfully” as we can’t distinguish between these comments and other normal comments. Secondly, we have had to reformat the output because we have only a “to-end-of-line” comment character. Thirdly, it looks horrible.

So, my minimal solution would be this; we introduce some new comment characters, which are treated as comments normally, but which carry enough semantics to allow a warning when they are wrong; rather like Javadoc, which is a comment wrt the language, but is structured and meaningful wrt the documentation. Tooling could be used to check that the comment masquerading labels are correct wrt to the identifiers.

Class: obo:OBI_1110161 [T cell epitope ELISA IL-1b assay],

    Annotations:
        rdfs:label "T cell epitope ELISA IL-1b assay"@en,

    SubClassOf:
        obo:OBI_0000661 [blah],
        obo:OBI_0000299 [longer blah]
        some (obo:IAO_0000109 [more]
        and (obo:IAO_0000136 [stuff]
        some obo:OBI_11101 [OBI Thing]
        ))

This is still not ideal; it would require extension to Manchester syntax, but it’s minimal, and it does support the semantics free identifiers in OBO in a way which does not require extensive tooling. It’s worth reiterating here that OBOs semantics-free identifiers are a good thing; so, supporting them supports others people who may wish to do the same, sensible thing. It does have the disadvantages of duplicating information, but at least in a way that is checkable.

Comments welcome!

Pilsen (or Plzen) is, perhaps, a strange place for the meeting. It’s a bit of a pig to get to, as the airport is in Prague. Likewise, the conference centre was a bit out of town, so you had to get a taxi if you wanted food in the evening. Still the venue itself worked well. Slightly flaky wireless, but it had tables upstairs on a balcony; a lot of people migrated up there as the meeting went on, making the auditorium a little deserted.

Although, I’ve said I didn’t understand lots of it, many of the keynotes this year were bioinformatics, systems biology or data integration which I know well. As well as that, there was a (semantic) web and ontology section. I enjoyed Tim Clarks talk, as he’s made stuff that lots of people are actually using, although I don’t think he explained why during his talk.

The section of high performance computing was probably the least relevant. While they’ve become interested in power consumption recently, these guys are still obsessed with teraflops (…now petaflops…now exaflops). To be honest, I don’t care. With more power, you can build more granular, higher resolution models, but I doubt that will bring you anything, unless you also have more granular data. They should be worried about discs — always the Cindarella of the hardware world, only slightly more interesting than printers — but it’s discs which carry the data. While we are at it, spinning discs use lots of power. And they have more flashing lights than CPUs. The hardware guys should be talking about disc space. The neuroscientists should be worrying about filling discs up. Neuroinformaticians should make that they end up with an exabyte dataset; not 1000 petabyte datasets or worse, 1,000,000 gigabyte datasets.

I tried to get a bit of Web 2.0 stuff happening at the meeting. David Sutherland set up a friendfeed room. Second day, we were sitting next to each other like two sad blokes at a party full of women, sending each other messages on their iphones. Although, it was a neuroinformatics meeting, so largely without the women. Second day, mostly it was just me, sad, lonely and pathetic. Still, having said that, I did manage to meet almost all of those subscribed to the room, which you couldn’t achieve at ISMB nowadays. Pavan Ramkumar said hello at lunch, and then later at the airport. I met Sarah Maynard at her poster; it had ontologies, OWL and information content-based similarity measures; bound to make me happy. Only Lisa Kjonigsen remained in cyberspace only. With luck, next year, more people will join; not least because I’ll probably not go to Japan.

I had a quick go at live blogging also; to be honest, I am not a natural. The problem is I have too much desire to editorialise. The roboblogger tells me that she just blogs the notes that she would have taken anyway; my notes, on the other hand, are full of comment, invective and questions. Perhaps I could just put these into the asciidoc source of my blog as comments. I stopped live blogging on the last day, not for these reasons, but largely as a desire not to hold my crushing ignorance of the topics being discussed up to public scrutiny.

Neuroinformatics (the meeting) is changing. I have to believe that if there is more about genomic and multiomic data integration that this has to be a good thing. The brain is a hard to thing to figure out; I have to believe that using more data, more types of data and a heavier use of nice, simple, model organisms is going to increase the rate of advance; with all the fuss about systems biology, it’s easy to forget the fabulous success of the last 100 years of reductionism biology, which made systems biology possible. This has to be the way forward for neuroscience. Even if it does make the meeting more usual and, perhaps, less interesting for me as a result.

Data management: View from 50,000 feet — dimensions are amount of structure and the number of data sources. More structure, less data sources.

Distinguishes between parallelisation and heterogeneity. Can distribute data across tables in an organised way — this is parallelisation; or, you can have lots of data, spread across resources, with multiple entities and with no common plan.

Outline — data integration and suggest data spaces as a solution.

Databases are so successful because it provides a level of abstraction over the data. Data integration is a higher level of abstraction still because you don’t have to worry how the data is stored or structured.

Mediated schema, uses a mediation language, a mapping tool, and then a set of wrappers over the datasources, which map them to a common syntax (relational database for example).

So, we know how to do it, but the cost of building data integration systems are really high. Creating the mediated schema or ontology is hard; sometimes it’s impossible. Mapping source to mediated schema can be a nightmare, because you need many people from both sides of the mediation. Are some automated systems, but human is always needed. Data level mappings (changing IDs, synonyms and so on). Social costs.

One of the problems with data integration is that it costs a lot early, but yields very little till quite a long time on, and it’s all done. What we really want is pay-as-you-go data management; want useful data out early and constantly.

Everytime human does something with data, they are telling you some information about the data. If you can capture this information then you can useful stuff with this.

Structured data on the web: the deep web, which is data behind forms; and two others. So, deep web. Knowledge which is not accessible through general purpose search engines — cars, houses and so on are examples of this. Uses data spaces as a way of doing this; learned different 5000 data sources in two months.

One possible way to access the deep web is to put queries against web forms. Have to guess what to put in; one way is to just use words on the form page in the first place. Currently, google gives much knowledge from this deep web; has the biggest impact on the deep web.

Web tables; can we exploit the knowledge from the tables better. There are 14billion tables on the web, of which about 154million are interesting — rest formatting or whatever. First problem is to identify schema elements; these are expressible in HTML but actually no one uses it. So have to guess. They got 2.6 million schemas. Would be good to put these into automcomplete (although not sure where).

Fusion tables lets you upload data and collaborate on the visualisation of it. Changes the visualisation options depending on the data types.

Conclusions — bottom up data-integration, which is more realistic than top-down. Dataspaces are an approach. Fusion tables is good.

Highest level NIF registry. Web index of resources which are relevant to neurosciences.

NIF resource diversity — three different levels of data, with increasing amount of structure.

Is GRM1 in cerebral cortex? NIF system allows searching over multiple different resources. But problems; inconsistent and sparse annotation of scientific data. Many different names of the same thing and so on. Added to this there are over 2000 databases in the registry.

Uses mixed searching so that both ontological information and string based systems important for where there is no annotation. Can also do query expansion with ontology to get better querying.

Building ontologies is difficult even for limited domains, never mind all of neurosciences. Trying to do this with multiple levels. NeuroLex — single inheritance, lexicon. NIFSTD, standardize modules under same upper ontology. NIFPlus — create intra-domain and more useful hierarchies using properties and restrictions. .

Using logical classification as a result of properties of the entities.

Question — how to get the community involved. Need to provide an easy to use platform for community collaboration. They have a semantic wiki for contributing to neurolex. Really lowers the barries for entry for domain experts who wish to use (and extend!) these terms.

Lots of people are starting to use the resources (they find this out because people complain when the systems are broken!).

Contributing to Neurolex. Don’t need an account, but better if you have one, everything online. Many thing that they are looking at is content, content, content. More stuff the better. Finally, getting people to value ontologists is really important.

Motivation, what is the common feature of a set of disorders. They are all complex disorders, which we don’t really understand.

Alzforum is a nice example of an early web community. Alzheimers forum. Works as an ongoing journal club, with curated discussions. Started off during the early days of the web.

Developed StemBook which is an online book, launched about a year ago. Discussion of stuff that is happening. pd online research, is another alzheimers website, using a toolkit that they have developed. Linking across these forums can be a problem; need some forms of shared terminology server. Science Collaboration Framework. Based around drupal, allows common collaborative tools for biomedicine, shared ontologies/vocabulary and so on.

How do you link between these communities? Issues of semantic annotation; how does this happen? Are systems which allow you to guess what an ontology is; building system which should work across lots of different content management faciltiies. This can bring lots of benefits, as the additional semantics allows you to work around synonyms etc.

Discourse ontologies. SALT — semantic annotation of latex.

Need to support a spectrum of different knowledge structures, theasurai and so on. Less complex == more tractable to biologists. Complex and formalised, tractable to computers.

Are now integration discourse ontology into myexperiment and others.

Using existing work on entity recognition and try and produce a provenance aware representation of these results.

Creative Commons is based around issues with data and copyright, trying to change the idea that not sharing is the default. Science Commons looks at the issues specific to science.

Semantic web in a nutshell; adds to web standards and practices encouraging, common naming, ontology development, expression in knowledge representation language, easy integration over multiple sources, works both inside and outside the organisational boundaries.

Why should you want this? Network effects, people can use their own skills, and combine knowledge from many different sources. Provides efficiencies at the global scale.

Copy and paste for the semantic web; a mashup with knowledge from Allen brain institute, and google API. Had to screenscrape Allen brain for this.

Trying to look for druggable targets in pyramidal neurons. Google provides too many results, so does pubmed. Shows complex SPARQL query over the knowledge from the web; crossing from MESH to gene to GO. This may not be the best query, but it’s none the less useful and will make biologists happy.

A brief jump into ontology making. Terms that mix up material and neurotransmitter. Uses example, peptide, neurotransmitter, hormone and ligand; all of these could be peptides, although not necessarily. Need to untangle these. In many cases, these have already been done (ChEBI). Move from English to OWL.

How to build consensus in ontology building — somewhat related to OBOFoundary rules. Another program is INCF program for ontology of neural structures.

Challenges — building bigger ontologies is hard. Barrier to sharing are a major difficulty.