## Abstract

Bio-medical ontologies can contain a large number of concepts. Often many of these concepts are very similar to each other, and similar or identical to concepts found in other bio-medical databases. This presents both a challenge and opportunity: maintaining many similar concepts is tedious and fastidious work, which could be substantially reduced if the data could be derived from pre-existing knowledge sources. In this paper, we describe how we have achieved this for an ontology of the mitochondria using our novel ontology development environment, the Tawny-OWL library.

• Jennifer D. Warrender
• Phillip Lord

## Plain English Summary

Ontologies allow complex descriptions of the world in a way that is both precise and computationally amenable — that is, computers can be used to check and query these descriptions. The mitochondria is a critical part of the cells of most organisms, being responsible for energy usage. We wished to build an ontology describing the current research on the mitochondria.

The more traditional approach to this, would have been to build the ontology from scratch; but many parts of the mitochondria, including the genes and proteins have already been described in other databases. Building from scratch on the basis of the data in these databases would be time-consuming, but also sensitive to change — if the database changes, our ontology would need updating too.

Instead we have used our new ontology development methodology to automatically extract this knowledge, and build the ontology for us providing what we describe as the scaffold for an ontology. In future, we will add more knowledge to this ontology, slowing building up the rich description of the mitochondrion that we are aiming for.

## Abstract

Ontology development relates to software development in that they both involve the production of formal computational knowledge. It is possible, therefore, that some of the techniques used in software engineering could also be used for ontologies; for example, in software engineering testing is a well-established process, and part of many different methodologies. The application of testing to ontologies, therefore, seems attractive. The Karyotype Ontology is developed using the novel Tawny-OWL library. This provides a fully programmatic environment for ontology development, which includes a complete test harness. In this paper, we describe how we have used this harness to build an extensive series of tests as well as used a commodity continuous integration system to link testing deeply into our development process; this environment, is applicable to any OWL ontology whether written using Tawny-OWL or not. Moreover, we present a novel analysis of our tests, introducing a new classification of what our different tests are. For each class of test, we describe why we use these tests, also by comparison to software tests. We believe that this systematic comparison between ontology and software development will help us move to a more agile form of ontology development.

• Jennifer D. Warrender
• Phillip Lord

## Plain English Summary

Ontologies are a mechanism for representing parts of the world computationally. They allow you to describe the world in a complex way, and then query over it repeatable and consistently. However, ontologies are complex and are themselves hard to build consistently and repeatably. If the ontology is built incorrectly, then queries will give the wrong answers also.

Software is also complex and over the years, software engineers have developed many techniques for building software so that it, too, is correct. While these do not always succeed, they have allowed us to produce software that is vastly more complex than in years past. One important technique is automated testing. Here software can be run to ensure that it is behaving correctly automatically and often. To do this, we use one piece of software to test another.

We have borrowed the same technology for use with ontologies; while this has been done before, our use of commodity testing software has allowed us to scale up the tests significantly, and we describe this approach in this paper. However, while they have many similarities, ontologies are not software. The sort of tests that we need for ontologies may be different from those that we need for software. In this paper, we also describe the kinds of tests that we have used for the karyotype ontology , and which are probably relevant to other ontology development efforts too.

Overall, this should increase our understanding of how to build ontology tests and ontologies.

## Abstract

A constant influx of new data poses a challenge in keeping the annotation in biological databases current. Most biological databases contain significant quantities of textual annotation, which often contains the richest source of knowledge. Many databases reuse existing knowledge, during the curation process annotations are often propagated between entries. However, this is often not made explicit. Therefore, it can be hard, potentially impossible, for a reader to identify where an annotation originated from. Within this work we attempt to identify annotation provenance and track its subsequent propagation. Specifically, we exploit annotation reuse within the UniProt Knowledgebase (UniProtKB), at the level of individual sentences. We describe a visualisation approach for the provenance and propagation of sentences in UniProtKB which enables a large-scale statistical analysis. Initially levels of sentence reuse within UniProtKB were analysed, showing that reuse is heavily prevalent, which enables the tracking of provenance and propagation. By analysing sentences throughout UniProtKB, a number of interesting propagation patterns were identified, covering over 100, 000 sentences. Over 8000 sentences remain in the database after they have been removed from the entries where they originally occurred. Analysing a subset of these sentences suggest that approximately 30% are erroneous, whilst 35% appear to be inconsistent. These results suggest that being able to visualise sentence propagation and provenance can aid in the determination of the accuracy and quality of textual annotation. Source code and supplementary data are available from the authors website.

• Michael J. Bell
• Matthew Collison
• Phillip Lord

## Plain English Summary

There are many database resources which describe biological entities such as proteins, and genes available to the researcher. These are used by both biologists and medics to understand how biological systems work which has implications for many areas. These databases store information of various sorts, called annotation: some of this is highly organised or structured knowledge; some is free text, written in English.

The quantity of this material available means that having a computation method to check the annotation is desirable. The structured knowledge is easier to check because it is organised. The free text knowledge is much harder.

Most methods of analysing free text are based around “normal” English; biological annotation uses a highly specialised form of English, heavily controlled and with many jargon words. In this paper, we exploit this specialised form to infer provenance, to understand when sentences were first added to the database, and how they change over time. By analysing these patterns of provenance, we were able to identify patterns which are indicative of inconsistency or erroneous annotation.

## Abstract

Semantic publishing can enable richer documents with clearer, computationally interpretable properties. For this vision to become reality, however, authors must benefit from this process, so that they are incentivised to add these semantics. Moreover, the publication process that generates final content must allow and enable this semantic content. Here we focus on author-led or "grey" literature, which uses a convenient and simple publication pipeline. We describe how we have used metadata in articles to enable richer referencing of these articles and how we have customised the addition of these semantics to articles. Finally, we describe how we use the same semantics to aid in digital preservation and non-repudiability of research articles.

• Phillip Lord
• Lindsay Marshall

## Plain English Summary

Academic literature makes heavy of references; effectively links to other, previous work that supports, or contradicts the current work. This referencing is still largely textual, rather than using a hyperlink as is common on the web. As well as being time consuming for the author, it also difficult to extract the references computationally, as the references are formatted in many different ways.

Previously, we have described a system which works with identifiers such as ArXiv IDs (used to reference this article above!), PubMed IDs and DOIs. With this system, called kcite, the author supplies the ID, and kcite generates the reference list, leaving the ID underneath which is easy to extract computationally. The data used to generate the reference comes from specialised bibliographic servers.

In this paper, we describe two new systems. The first, called Greycite, provides similiar bibliographic data for any URL; it is extracted from the URL itself, using a wide variety of markup and some ad-hoc tricks, which the paper describes. As a result it works on many web pages (we predict about 1% of the total web, or a much higher percentage of “interesting” websites). Our second system, kblog-metadata, provides a flexible system for generating this data. Finally, we discuss ways in which the same metadata can be used for digitial preservation, by helping to track articles as and when they move across the web.

This paper was first written for the Sepublica 2013 workshop.

## Abstract

The Tawny-OWL library provides a fully-programmatic environment for ontology building; it enables the use of a rich set of tools for ontology development, by recasting development as a form of programming. It is built in Clojure - a modern Lisp dialect, and is backed by the OWL API. Used simply, it has a similar syntax to OWL Manchester syntax, but it provides arbitrary extensibility and abstraction. It builds on existing facilities for Clojure, which provides a rich and modern programming tool chain, for versioning, distributed development, build, testing and continuous integration. In this paper, we describe the library, this environment and the its potential implications for the ontology development process.

• Phillip Lord

## Plain English Summary

In this paper, I describe some new software, called Tawny-OWL, that addresses the issue of building ontologies. An ontology is a formal hierarchy, which can be used to describe different parts of the world, including biology which is my main interest.

Building ontologies in any form is hard, but many ontologies are repetitive, having many similar terms. Current ontology building tools tend to require a significant amount of manual intervention. Rather than look to creating new tools, Tawny-OWL is a library written in full programming language, which helps to redefine the problem of ontology building to one of programming. Instead of building new ontology tools, the hope is that Tawny-OWL will enable ontology builders to just use existing tools that are designed for general purpose programming. As there are many more people involved in general programming, many tools already exist and are very advanced.

This is the first paper on the topic, although it has been discussed before here.

This paper was written for the OWLED workshop in 2013.

## Reviews

Reviews are posted here with the kind permission of the reviewers. Reviewers are identified or remain anonymous (also to myself) at their option. Copyright of the review remains with the reviewer and is not subject to the overall blog license. Reviews may not relate to the latest version of this paper.

### Review 1

The given paper is a solid presentation of a system for supporting the development of ontologies – and therefore not really a scientific/research paper.

It describes Tawny OWL in a sufficiently comprehensive and detailed fashion to understand both the rationale behind as well as the functioning of that system. The text itself is well written and also well structured. Further, the combination of the descriptive text in conjunction with the given (code) examples make the different functionality highlights of Tawny OWL very easy to grasp and appraise.

As another big plus of this paper, I see the availability of all source code which supports the fact that the system is indeed actually available – instead of being just another description of a “hidden” research system.

The possibility to integrate Tawny OWL in a common (programming) environment, the abstraction level support, the modularity and the testing “framework” along with its straightforward syntax make it indeed very appealing and sophisticated.

But the just said comes with a little warning: My above judgment (especially the last comment) are highly biased by the fact that I am also a software developer. And thus I do not know how much the above would apply to non-programmers as well.

And along with the above warning, I actually see a (more global) problem with the proposed approach to ontology development: The mentioned “waterfall methodologies” are still most often used for creating ontologies (at least in the field of biomedical ontologies) and thus I wonder how much programmatic approaches, as implemented by Tawny OWL, will be adapted in the future. Or in which way they might get somehow integrated in those methodologies.

### Review 2

This review is by Bijan Parsia.

This paper presents a toolkit for OWL manipulation based on Clojure. The library is interesting enough, although hardly innovative. The paper definitely oversells it while neglecting details of interest (e.g., size, facilities, etc.). It also neglects relevant related work, Thea-OWL, InfixOWL, even KRSS, KIF, SXML, etc.

I would like to seem some discussion of the challenges of making an effect DSL for OWL esp. when you incorporate higher abstractions. For example, how do I check that a generative function for a set of axioms will always generate an OWL DL ontology? (That seems to be the biggest programming language theoretic challenge.)

Some of the dicussion is rather cavalier as well, e.g.,

“Alternatively, the ContentCVS system does support oine concurrent mod-ication. It uses the notion of structural equivalence for comparison and resolution of conflicts[4]; the authors argue that an ontology is a set of axioms. However, as the named suggests, their versioning system mirrors the capabilitiesof CVS { a client-server based system, which is now considered archaic.”

I mean, the interesting part of ContentCVS is the diffing algorithm (note that there’s a growing literature on diff in OWL). This paper focuses on the inessential aspect (i.e., really riffing off the name) and ignores the essential (i.e., what does diff mean). Worse, to the degree that it does focus on that, it only focuses on the set like nature of OWL according to the structural spec. The challenges of diffing OWL (e.g., if I delete an axiom have I actually deleted it) are ignored.

Finally, the structural specification defines an API for OWL. It would be nice to see a comparison and/or critique.

### Three Steps to Heaven

Phillip Lord, Simon Cockell and Robert Stevens
School of Computing Science, Newcastle University,
Newcastle-upon-Tyne, UK
Bioinformatics Support Unit, Newcastle University,
Newcastle-upon-Tyne, UK
School of Computer Science, University of Manchester, UK
phillip.lord@newcastle.ac.uk

Semantic publishing offers the promise of computable papers, enriched visualisation and a realisation of the linked data ideal. In reality, however, the publication process contrives to prevent richer semantics while culminating in a ‘lumpen’ PDF. In this paper, we discuss a web-first approach to publication, and describe a three-tiered approach which integrates with the existing authoring tooling. Critically, although it adds limited semantics, it does provide value to all the participants in the process: the author, the reader and the machine.

# 1 Introduction

The publishing of both data and narratives on those data are changing radically. Linked Open Data and related semantic technologies allow for semantic publishing of data. We still need, however, to publish the narratives on that data and that style of publishing is in the process of change; one of those changes is the incorporation of semantics . The idea of semantic publishing is an attractive one for those who wish to consume papers electronically; it should enhance the richness of the computational component of papers . It promises a realisation of the vision of a next generation of the web, with papers becoming a critical part of a linked data environment ,, where the results and naratives become one.

The reality, however, is somewhat different. There are significant barriers to the acceptance of semantic publishing as a standard mechanism for academic publishing. The web was invented around 1990 as a light-weight mechanism for publication of documents. It has subsequently had a massive impact on society in general. It has, however, barely touched most scientific publishing; while most journals have a website, the publication process still revolves around the generation of papers, moving from Microsoft Word or LaTeX , through to a final PDF which looks, feels and is something designed to be printed onto paper (this includes conferences dedicated to the web and the use of web technologies). Adding semantics into this environment is difficult or impossible; the content of the PDF has to be exposed and semantic content retro-fitted or, in all likelihood, a complex process of author and publisher interaction has to be devised and followed. If semantic data publishing and semantic publishing of academic narratives are to work together, then academic publishing needs to change.

In this paper, we describe our attempts to take a commodity publication environment, and modify it to bring in some of the formality required from academic publishing. We illustrate this with three exemplars—different kinds of knowledge that we wish to enhance. In the process, we add a small amount of semantics to the finished articles. Our key constraint is the desire to add value for all the human participants. Both authors and readers should see and recognise additional value, with the semantics a useful or necessary byproduct of the process, rather than the primary motivation. We characterise this process as our “three steps to heaven”, namely:

• make life better for the machine to

• make life better for the author to

• make life better for the reader

While requiring additional value for all of these participants is hard, and places significant limitations on the level of semantics that can be achieved, we believe that it does increase the likelihood that content will be generated in the first place, and represents an attempt to enable semantic publishing in a real-world workflow.

# 2 Knowledgeblog

The knowledgeblog project stemmed from the desire for a book describing the many aspects of ontology development, from the underlying formal semantics, to the practical technology layer and, finally, through to the knowledge domain . However, we have found the traditional book publishing process frustrating and unrewarding. While scientific authoring is difficult in its own right, our own experience suggests that the publishing process is extremely hard-work. This is particularly so for multi-author collected works which are often harder for the editor than writing a book “solo”. Finally, the expense and hard copy nature of academic books means that, again in our experience, few people read them.

This contrasts starkly with the web-first publication process that has become known as blogging. With any of a number of ready made platforms, it is possible for authors with little or no technical skill, to publish content to the web with ease. For knowledgeblog (“kblog”), we have taken one blogging engine, WordPress , running on low-end hardware, and used it to develop a multi-author resource describing the use of ontologies in the life sciences (our main field of expertise). There are also kblogs on bioinformatics and the Taverna workflow environment . We have previously described how we addressed some of the social aspects, including attribution, reviewing and immutablity of articles

As well as delivering content, we are also using this framework to investigate semantic academic publishing, investigating how we can enhance the machine interpretability of the final paper, while living within the key constraint of making life (slightly) better for machine, author and reader without adding complexity for the human participants.

Scientific authors are relatively conservative. Most of them have well-established toolsets and workflows which they are relatively unwilling to change. For instance, within the kblog project, we have used workshops to start the process of content generation. For our initial meeting, we gave little guidance on authoring process to authors, as a result of which most attempted to use WordPress directly for authoring. The WordPress editing environment is, however, web-based, and was originally designed for editing short, non-technical articles. It appeared to not work well for most scientists.

The requirements that authors have for such ‘scientific’ articles are manifold. Many wish to be able to author while offline (particularly on trains or planes). Almost all scientific papers are multi-author, and some degree of collaboration is required. Many scientists in the life sciences wish to author in Word because grant bodies and journals often produce templates as Word documents. Many wish to use LaTeX, because its idiomatic approach to programming documents is unreplicable with anything else. Fortunately, it is possible to induce WordPress to accept content from many different authoring tools, including Word and LaTeX

As a result, during the kblog project, we have seem many different workflows in use, often highly idiosyncratic in nature. These include:

Word/Email:

Many authors write using MS Word and collaborate by emailing files around. This method has a low barrier to entry, but requires significant social processes to prevent conflicting versions, particularly as the number of authors increases.

Word/Dropbox:

For the taverna kblog , authors wrote in Word and collaborated with Dropbox . This method works reasonably well where many authors are involved; Dropbox detects conflicts, although it cannot prevent or merge them.

Asciidoc/Dropbox:

Used by the authors of this paper. Asciidoc is relatively simple, somewhat programmable and accessible. Unlike LaTeX which can be induced to produce HTML with effort, asciidoc is designed to do so.

Of these three approaches probably the Word/Dropbox combination is the the most generally used.

From the readers perspective, a decision that we have made within knowledgeblog is to be “HTML-first”. The initial reasons for this were entirely practical; supporting multiple toolsets is hard, particularly if any degree of consistency is to be maintained; the generation of the HTML is at least partly controlled by the middleware – WordPress in kblog’s case. As well as enabling consistency of presentation, it also, potentially, allows us to add additional knowledge; it makes semantic publication a possibility. However, we are aware that knowledgeblog currently scores rather badly on what we describe as the “bath-tub test”; while exporting to PDF or printing out is possible, the presentation is not as “neat” as would be ideal. In this regard (and we hope only in this regard), the knowledgeblog experience is limited. However, increasingly, readers are happy and capable of interacting with material on the web, without print outs.

From this background and aim, we have drawn the following requirements:

1. The author can, as much as possible, remain within familiar authoring environments;

2. The representation of the published work should remain extensible to, for instance, semantic enhancements;

3. The author and reader should be able to have the amount of “formal” academic publishing they need;

4. Support for semantic publishing should be gradual and offer advantages for author and reader at all stages.

We describe how we have achieved this with three exemplars, two of which are relatively general in use, and one more specific to biology. In each case, we have taken a slightly different approach, but have fulfilled our primary aim of making life better for machine, author and reader.

# 3 Representing Mathematics

The representation of mathematics is a common need in academic literature. Mathematical notation has grown from a requirement for a syntax which is highly expressive and relatively easy to write. It presents specific challenges because of its complexity, the difficulty of authoring and the difficulty of rendering, away from the chalk board that is its natural home.

Support for mathematics has had a significant impact on academic publishing. It was, for example, the original motivation behind the development of TeX , and it still one of the main reasons why authors wish to use it or its derivatives. This is to such an extent that much mathematics rendering on the web is driven by a TeX engine somewhere in the process. So MediaWiki (and therefore Wikipedia), Drupal and, of course, WordPress follow this route. The latter provides plugin support for TeX markup using the wp-latex plugin . Within kblog, we have developed a new plugin called mathjax-latex From the kblogauthor’s perspective these two offer a similar interface – differences are, therefore, described later.

Authors write their mathematics directly as TeX using one of the four markup syntaxes. The most explicit (and therefore least likely to happen accidentally) is through the use of “shortcodes” .

These are a HTML-like markup originating from some forum/bulletin board systems. In this form an equation would be entered as $e=mc^2$, which would be rendered as “$$e=mc^2$$”. It is also possible to use three other syntaxes which are closer to math-mode in TeX: $‍$e=mc^2$‍$, $latex e=mc^2$, or \‍[e=mc^2\‍].

From the authorial perspective, we have added significant value, as it is possible to use a variety of syntaxes, which are independent of the authoring engine. For example, a TeX-loving mathematician working with a Word-using biologist can still set their equations using TeX syntax; although Word will not render these at authoring time but, in practice, this causes few problems for such authors, who are experienced at reading TeX. Within an LaTeX workflow equations will be renderable both locally with source compiled to PDF, and published to WordPress.

There is also a W3C recommendation, MathML for the representation and presentation of mathematics. The kblog environment also supports this. In this case, the equivalent source appears as follows:

$<mrow> <mi>E</mi> <mo>=</mo> <mrow> <mi>m</mi> <msup> <mi>c</mi> <mn>2</mn> </msup> </mrow> </mrow>$

One problem with the MathML representation is obvious: it is very long-winded. A second issue, however, is that it is hard to integrate with existing workflows; most of the publication workflows we have seen in use will on recognising an angle bracket turn it into the equivalent HTML entity. For some workflows (LaTeX, asciidoc) it is possible, although not easy, to prevent this within the native syntax.

It is also possible to convert from Word’s native OMML (“equation editor”) XML representation to MathML, although this does not integrate with Word’s native blog publication workflow. Ironically, it is because MathML shares an XML based syntax with the final presentation format (HTML) that the problem arises. The shortcode syntax, for example, passes straight-through most of the publication frameworks to be consumed by the middleware. From a pragmatic point of view, therefore, supporting shortcodes and TeX-like syntaxes has considerable advantages.

For the reader, the use of mathjax-latex has significant advantages. The default mechanism within WordPress uses a math-mode like syntax $‍latex e=mc^2‍$. This is rendered using a TeX engine into an image which is then incorporated and linked using normal HTML capabilities. This representation is opaque and non-semantic; it has significant limitations for the reader. The images are not scalable – zooming in cases severe pixalation; the background to the mathematics is coloured inside the image, so does not necessarily reflect the local style.

Kblog, however, uses the MathJax library this has a number of significant advantages for the reader. First, where the browser supports them, MathJax uses webfonts to render the images; these are scalable, attractive and standardized. Where they are not available, MathJax can fall-back to bitmapped fonts. The reader can also access additional functionality: clicking on an equation will raise a zoomed in popup; while the context menu allows access to a textual representation either as TeX or MathML irrespective of the form that the author used. This can be cut-and-paste for further use. Kblog uses the MathJax library to render the underlying TeX directly on the client.

Our use of MathJax provides no significant disadvantages to the middleware layers. It is implemented in JavaScript and runs in most environments. Although, the library is fairly large (>100Mb), but is available on a CDN so need not stress server storage space. Most of this space comes from the bit-mapped fonts which are only downloaded on-demand, so should not stress web clients either. It also obviates the need for a TeX installation which wp-latex may require (although this plugin can use an external server also).

At face value, mathjax-latex necessarily adds very little semantics to the maths embedded within documents. The maths could be represented as $‍$E=mc^2$‍$, \‍(E=mc^2\‍) or

$<mrow> <mi>E</mi> <mo>=</mo> <mrow> <mi>m</mi> <msup> <mi>c</mi><mn>2</mn> </msup> </mrow> </mrow>$

So, we have a heterogenous representation for identical knowledge. However, in practice, the situation is much better than this. The author of the work created these equations and has then read them, transformed by MathJax into a rendered form. If MathJax has failed to translate them correctly, in line with the author’s intention, or if it has had some implications for the text in addition to setting the intended equations (if the TeX style markup appears accidentally elsewhere in the document), the author is likely to have seen this and fixed the problem. Someone wishing, for example, to extract all the mathematics as MathML from these documents computationally, therefore, knows:

• that the document contains maths as it imports MathJax

• that MathJax is capable of identifying this maths correctly

• that equations can be transformed to MathML using MathJax (This is assuming MathJax works correctly in general. The authors and readers are checking the rendered representation. It is possible that an equation would render correctly on screen, but be rendered to MathML inaccurately).

So, while our publication environment does not result directly in lower level of semantic heterogeneity, it does provide the data and the tools to enable the computational agent to make this transformation. While this is imperfect, it should help a bit. In short, we provide a practical mechanism to identify text containing mathematics and a mechanism to transform this to a single, standardised representation.

# 4 Representing References

Unlike mathematics, there is no standard mechanism for reference and in-text citation, but there are a large number of tools for authors such as BibTeX, Mendeley or EndNote. As a result of this, the integration with existing toolsets is of primary importance, while the representation of the in-text citations is not, as it should be handled by the tool layer anyway.

Within kblog, we have developed a plugin called kcite . For the author, citations are inserted using the syntax:[‍cite]10.1371/journal.pone.0012258[‍/cite]. The identifier used here is a DOI, or digital object identifier and, is widely used within the publishing and library industry. Currently, kcite supports DOIs minted by either CrossRef or DataCite (in practice, this means that we support the majority of DOIs). We also support identifiers from PubMed which covers most biomedical publications and arXiv , the physics (and other domains!) preprints archive, and we now have a system to support arbitrary URLs. Currently, authors are required to select the identifier where it is not a DOI.

We have picked this “shortcode” format for similar reasons as described for maths; it is relatively unambiguous, it is not XML based, so passes through the HTML generation layer of most authoring tools unchanged and is explicitly supported in WordPress, bypassing the need for regular expressions and later parsing. It would, however, be a little unwieldy from the perspective of the author. In practice, however, it is relatively easy to integrate this with many reference managers. For example, tools such as Zotero and Mendeley use the Citation Style Language, and so can output kcite compliant citations with the following slightly elided code:

<citation>
<layout prefix="[‍cite]" suffix="[‍/cite]"
delimiter="[‍/cite] [‍cite]">
<text variable="DOI"/>
</layout>
</citation>

We do not yet support LaTeX/BibTeX citations, although we see no reason why a similar style file should not be supported (citations in this representation of the article were, rather painfully, converted by hand). We do, however, support BibTeX-formatted files: the first author’s preferred editing/citation environment is based around these with Emacs, RefTeX, and asciidoc. While this is undoubtedly a rather niche authoring environment, the (slightly elided) code for supporting this demonstrates the relative ease with which tool chains can be induced to support kcite:

(defadvice reftex-format-citation (around phil-asciidoc-around activate)
(if phil-reftex-citation-override
(setq ad-return-value (phil-reftex-format-citation entry format))

(defun phil-reftex-format-citation( entry format )
(let ((doi (reftex-get-bib-field "doi" entry)))
(format "pass:[‍[‍cite source='doi'\\]%s[‍/cite\\]]" doi)))

The key decision with kcite from the authorial perspective is to ignore the reference list itself and focus only on in-text citations, using public identifiers to references. This simplifies the tool integration process enormously, as this is the only data that needs to pass from the author’s bibliographic database onward. The key advantage for authors here is two-fold: they are not required to populate their reference metadata for themselves, and this metadata will update if it changes. Secondly, the identifiers are checked; if they are wrong, the authors will see this straightforwardly as the entire reference will be wrong. Adding DOIs or other identifiers moves from becoming a burden for the author to becoming a specific advantage.

While supporting multiple forms of reference identifier (CrossRef DOI, DataCite DOI, arXiv and PubMed ID) provides a clear advantage to the author, it comes at considerable cost. While it is possible to get metadata about papers from all of these sources, there is little commonality between them. Moreover, resolving this metadata requires one outgoing HTTP request per reference (in practice, it is often more; DOI requests, for instance use 303 redirects), which browser security might or might not allow.

So, while the presentation of mathematics is performed largely on the client, for reference lists the kcite plugin performs metadata resolution and data integration on the server. A caching functionality is provided, storing this metadata in the WordPress database. The bibliographic metadata is finally transferred to the client encoded as JSON, using asynchronous call-backs to the server.

For the computational agent wishing to consume bibliographic information, we have added significant value compared to the pre-formatted HTML reference list. First, all the information required to render the citation is present in the in-text citation next to the text that the authors intended. A computational agent can, therefore, ignore the bibliography list itself entirely. These primary identifiers are, again, likely to be correct because the authors now need them to be correct for their own benefit.

Should the computational agent wish, the (denormalised) bibliographic data used to render the bibliography is actually available, present in the underlying HTML as a JSON string. This is represented in a homogeneous format, although, of course, represents our (kcite’s) interpretation of the primary data.

A final, and subtle, advantage of kcite is that the authors can only use public metadata, and not their own. If they use the correct primary identifier, and still get an incorrect reference, it follows that the public metadata must be incorrect (or, we acknowledge, that kcite is broken!). Authors and readers therefore must ask the metadata providers to fix their metadata to the benefit of all. This form of data linking, therefore, can even help those who are not using it.

## 4.1 Microarray Data

Many publications require that papers discussing microarray experiments lodge their data in a publically available resource such as ArrayExpress . Authors do this placing an ArrayExpress identifier which has the form E-MEXP-1551. Currently, adding this identifier to a publication, as with adding the raw data to the repository is no direct advantage to the author, other than fulfilment of the publication requirement. Similarly, there is no existing support within most authoring environments for adding this form of reference.

For the knowledgeblog-arrayexpress plugin , therefore, we have again used a shortcode representation, but allowed the author to automatically fill metadata, direct from ArrayExpress. So a tag such as:[‍aexp id="E-MEXP-1551"]species[‍/aexp] will be replaced with Saccharomyces cerevisiae, while:[‍aexp id="E-MEXP-1551"]releasedate[‍/aexp] will be replaced by “2010-02-24”. While the advantage here is small, it is significant. Hyperlinks to ArrayExpress are automatic, authors no longer need to look up detailed metadata. For metadata which authors are likely to know anyway (such as Species), the automatic lookup operates as a check that their ArrayExpress ID is correct. As with references (see Section ), the use of an identifier becomes an advantage rather than a burden to the authors.

Currently, for the reader there is less significant advantage at the moment. While there is some value to the author of the added correctness stemming from the ArrayExpress identifier. However, knowledgeblog-arrayexpress is currently under-developed, and the added semantics that is now present could be used more extensively. The unambiguous knowledge that:[‍aexp id="E-MEXP-1551"]species[‍/aexp] represents a species would allow us, for example, to link to the NCBI taxonomy database .

Likewise, advantage for the computational agent from knowledgeblog­-array­express is currently limited; the identifiers are clearly marked up, and as the authors now care about them, they are likely to be correct. Again, however, knowledgeblog­-array­express is currently under developed for the computational agent. The knowledge that is extracted from ArrayExpress could be presented within the HTML generated by knowledgeblog­-array­express, whether or not it is displayed to the reader for, essentially no cost. By having an underlying shortcode representation, if we choose to add this functionality to knowledgeblog­-array­express, any posts written using it would automatically update their HTML. For the text-mining bioinformatician, even the ability to unambiguously determine that a paper described or used a data set relating to a specific species using standardised nomenclature (the standard nomenclature was only invented in 1753 and is still not used universally) would be a considerable boon.

# 5 Discussion

Our approach to semantic enrichment of articles is a measured and evolutionary approach. We are investigating how we can increase the amount of knowledge in academic articles presented in a computationally accessible form. However, we are doing so in an environment which does not require all the different aspects of authoring and publishing to be over-turned. More over, we have followed a strong principle of semantic enhancement which offers advantages to both reader and author immediately. So, adding references as a DOI, or other identifier, ‘automagically’ produces an in text citation and a nicely formatted reference list: that the reference list is no longer present in the article, but is a visualisation over linked data; that the article itself has become a first class citizen of this linked data environment is a happy by-product.

This approach, however, also has disadvantages. There are a number of semantic enhancements which we could make straight-forwardly to the knowledgeblog environment that we have not; the principles that we have adopted requires significant compromise. We offer here two examples.

Second, our presentation of mathematics could be modified to automatically generate MathML from any included TeX markup. The transformation could be performed on the server, using MathJax; MathML would still be rendered on the client to webfonts. This would mean that any embedded maths would be discoverable because of the existence of MathML, which is a considerable advantage. However, neither the reader nor the author gain any advantage from doing this, while paying the cost of the slower load times and higher server load that would result from running JavaScript on the server. More over, they would pay this cost regardless of whether their content were actually being consumed computationally. As the situation now stands, the computational user needs to identify the insert of MathJax into the web page, and then transform the page using this library, none of which is standard. This is clearly a serious compromise, but we feel a necessary one.

Our support for microarrays offers the possibility of the most specific and increased level of semantics of all of our plugins. Knowledge about a species or a microarray experimental design can be precisely represented. However, almost by definition, this form of knowledge is fairly niche and only likely to be of relevance to a small community. However, we do note that the knowledgeblog process based around commodity technology does offer a publishing process that can be adapted, extended and specialised in this way relatively easily. Ultimately the many small communities that make up the long-tail of scientific publishing adds up to one large one.

# 6 Conclusion

Semantic publishing is a desirable goal, but goals need to be realistic and achievable. to move towards semantic publishing in kblog, we have tried to put in place an approach that gives benefit to readers, authors and computational interpretation. As a result, at this stage, we have light semantic publishing, but with small, but definite benefits for all.

Semantics give meaning to entities. In kblog, we have sought benefit by “saying” within the kblog environment that entity x is either maths, a citation or a microarray data entity reference. This is sufficient for the kbloginfra-structure to “know what to do” with the entity in question. Knowing that some publishable entity is a “lump” of maths tells the infra-structure how to handle that entity: the reader has benefit from it looking like maths; the author has benefit by not having to do very much; and the infra-structure knows what to do. In addition, this approach leaves in hooks for doing more later.