## Archive for the ‘Professional’ Category

### Tawny 1.0

I am please to announce the first full release of Tawny-OWL, my library for fully programmatic development of OWL ontologies. The library now has a fairly large feature set:

• Complete support for OWL2

• Integrated support for reasoning with HermiT or ELK

• Profile checking

• Fixtures and support macros for unit testing

• Use of external ontologies available only as OWL files

• Rendering of OWL API objects to Tawny code.

• Support for generating and using ontologies with numeric IDs.

• Support for multilingual labels.

Additionally, I now have initial integration with Protege, described later.

The library is now available from clojars or on github.

Feedback is welcome at tawny-owl@googlegroups.com.

• Background

A little over a year ago, I first described my experiments with building a programmatic environment for ontology construction . The need arose out of frustration with existing ontology tools; Protege, for example, provides a nice graphical environment, but it has many limitations. It does not easily allow automated generation of ontology entities, for example, and it also does not provide access to tools which are common place in an IDE: versioning tools, diffs, test cases and so forth. While ontology specific variations of these tools do exist, they were not as good as the ones I was used to use when programming.

Tawny seeks to bridge this gap, by using a full programmatic environment to generate OWL ontologies. I chose Clojure because of its syntactic plasticity; at its simplest, when using tawny, it does not feel like a programming language, just a syntax and evaluation engine for writing ontologies. However, the full power of the programming language is there and can be used when necessary .

Since the first blog post, there have now been a further 8, as well as three papers, describing tawny itself , the karyotype ontology and our use of patterns, higher-level abstractions within the karyotype ontology and applied to SIO. From an initial experiment, tawny has become a useful tool which we are using on a daily basis.

• In Early Release

Included in this release is our initial integration with Protege. Tawny builds on the OWL API, which is also the basis for Protege. I always assumed that Protege would be used to view OWL files generated by Tawny, but it is actually possible to integrate them much more comprehensively than this. It is now possible to directly manipulate the data structures of Protege using Tawny; in short, Protege can display what ever tawny has generated immediately, and without a file in between.

We have achieved this in two ways; firstly with protege-tawny which provides a command line environment directly inside Protege. This is useful, but does not provide the rich programmatic IDE that I want. However, the protege-nrepl environment allows exactly this; protege launches Clojure, and launches a NREPL server to which you connect with Emacs, Eclipse or any of the other Clojure IDEs. Finally, lein-sync allows syncing classpaths and dependencies with an existing Clojure project. The practical upshot can be seen in screencast; tawny can be used as normal, with Protege following.

Currently, Protege and Tawny use different versions of the OWL API, so while the protege-nrepl can be used with the current release of Protege, periodic crashes happen. In the meantime, a hand-built distribution of Protege is available including nrepl.

• For the Future

I have three main aims for the next few releases of Tawny. First, we need to provide access to explanation code; currently, this has to be accessed within Protege, which is less than ideal for a process than can take many minutes to run. I wish to integrate this with the Clojure unit test environment so that explanations will be generated by failing test cases.

Second, tawny currently allows the development of ontologies, but does not allow easy querying over them. I have several possibilities here: including integration of a SPARQL engine; the current rendering engine, combined perhaps with core.match, or, finally, fully-fledged support for core.match directly.

Finally, I wish to experiment with and add support for connection points , to better enable modular ontology development.

## Bibliography

### Ontology Connection Points

In this post, I will describe what I call connection points and explain how they can be used to enable modularity and overcome problems with scalability of reasoning in OWL.

One of the recurrent problems with building ontologies is mission creep; what starts simple rapidly expands until many different areas of the world are described.

I faced this problem recently, when I was asked about the axiomatisation that I described in my paper about function . Well, the axiomatisation exists, but it was never very complete; so, I thought I should redo it, probably with Tawny-OWL .

To start off with a simple declaration of function, we might choose something like this:

 (defclass Function :subclass (only realisedIn nProcess))

Or, in rough English, a function is something that displays itself only when involved in a process (the n in nProcess is to avoid a name clash). Now, immediately, we hit the mission-creep problem. Traditionally, functions have been considered to be some strain of continuant, and so it might be expected that we would only need to describe classes that are continuants to define a function. And, yet, straight away, we have a process. To make this definition meaningful, we need to distinguish between processes and everything else, and pretty quickly, our ontology of function requires most of an upper ontology.

This has important consequences. First, if the upper ontology in use is any size at all, or alternatively has a complex axiomatisation, then immediately a lot of axioms have to be reasoned over, and this can take considerable time.

Second, and probably more importantly, the choice of an upper ontology can be quite divisive. We have argued that a single representation for knowledge is neither plausible nor desirable  — this limits the ability to abstract, meaning that all of the complexity has to be dealt with all of the time; in essence, an extreme example of mission creep. If, for example, BFO is used, then the representation of entities whose existence we are unsure about becomes difficult. Conversely, if SIO is used, uncertain objects come regardless.

In the rest of this post, I will describe the how we can use the OWL import mechanism to define what I term connection points to work around this problem.

## Identifiers and Imports

One of the interesting things about OWL is that, as a web based system, it uses global identifiers in the form of IRIs (or URIs, or URLs, as you wish); I can make statements about your concepts, you can make statements about mine. However, not all OWL ontologies share the same axiom space; this is controlled explicitly, through the OWL import mechanism. In short, while you can make statements about my ontology, I do not have to listen. The practical upshot of this is that it is possible to share identifiers between two ontologies without sharing any axioms, or to share axioms in one direction only.

One nice use of this is with a little upper ontology that I built mostly to try out Tawny, called tawny.upper. This comes in two forms, one in EL profile, and one in DL; the latter has more semantics but is slower to reason over. The DL version imports the EL version but, unusually, introduces no new identifiers at all, it just refines the terms in the EL version with the desired additional semantics. Downstream users can switch between EL and DL semantics by simply adding or removing an OWL import statement.

## Alternative forms of import

The ability to share identifiers but not axioms has been used by others, as it provides a partial solution to the problem of big imports. MIREOT , for example, defines an alternative import mechanism. MIREOT is described as a minimal information standard ; in this it is rather simple, as the minimal information required to reference (identify) an ontology term its identifier and that of its ontology. In practice MIREOT is a set of tools that, at its simplest, involves sharing just the identifier and not the semantics. This can help to reduce the size of an ontology significantly.

An extreme use-case for this would be in our karyotype ontology ; if we wished “human” to refer to the NCBI taxonomy, we could import 100,000s of classes to use one, increasing the size of the ontology by several orders of magnitude in the process. One solution is to just use the identifier and not owl import the NCBI taxonomy.

However, this causes two problems. First, following our example we can no longer infer that, for example, a Human karyotype is a Mammalian karyotype; these semantics are present only in the NCBI taxonomy, and we must import its semantics if we wish to know this; similarly, we would be free to state that, for example, a human karyotype was also a fly karyotype. The second problem is that, in tools like Protege, the terms becomes unidentifiable, because the rdfs:label for the term has not been imported, and the NCBI taxonomy uses numeric identifiers.

The MIREOT solution is to extract a subset of the axioms in the upstream ontology, and then import these; obvious subsets would be all the labels of terms used in a downstream ontology, although MIREOT uses a slightly more complex system . This would solve the problem of terms being unidentifiable; still, though, human would not be known to be mammalian. Another subset would be all terms from mammal downwords (with their labels). Now, human would be known to be a mammal, but not known to not be a fly. As you increase the size of the subset, you increase the inferences that you can make, but the reasoning process will get slower.

From my perspective, the second of these seems sensible; large ontologies reason slowly and there is no way around this, until reasoner technology gets better. For this reason, I will probably implement something similar in tawny (with an improvement suggested later). The first, however, seems less justified. We are effectively duplicating all the labels in the upstream ontology, with all this entails, for the purpose of display; we can minimise these problems, by regularly regenerating the imported subset from the source ontology regularly, but this is another task that needs to be done.

Tawny is less affected by this from the start, since the name that a developer uses can exist only in Clojure space; more over, when displaying documentation, tawny can use data from any ontologies, rather than those imported into the current ontology. We do not need to duplicate the MIREOT subset, we just need to know about it.

## Connection Points

While MIREOT is a sensible idea, it is nonetheless seen as a workaround, a compromise solution to a difficult problem . However, in this section, I will discuss a simpler, and more general solution that helps to address the problem of modularity.

Consider, a reworked version of the definition above, with one critical change. The nProcess term is now referencing an independent Clojure namespace. The generated OWL from this ontology will include nProcess simply as a reference.

 (defclass Function :subclass (only realisedIn connection.upper/nProcess))

This is different from the MIREOT approach which maintains that the minimal information is the identifier for the term and the identifier for the ontology. In this case, we only have the former. This difference is important, as I will describe later.

In one sense, we have achieved something negative. We now have a term in our function ontology, with no semantics or annotations. Oops has this in their catalogue of ontology errors:

P8. Missing annotations: ontology terms lack annotations properties. This kind of properties improves the ontology understanding and usability from a user point of view.

— OOPS

However, this problem can be fixed by the editing environment; and, indeed, using Tawny it is. We have a meaningful name, despite a meaningless identifier, and we can see the definition of nProcess should we choose. I call these form of references connectors, and they have interesting properties. In this case, using nProcess is a required connector. The function ontology needs it to have its full semantic meaning, but it is not provided.

So, let us consider how we might use these connection points. First, for this example, we need a small upper ontology; in this case, I use the simplest possible ontology to demonstrate my point.

 (defontology upper) (as-disjoint (defclass nProcess) (defclass NotProcess))

Now, considering our function definition earlier; imagine that we wish to use this in a downstream ontology to define some functions. In this case, we define a child of Function which is realisedIn something which is NotProcess. The simplest possible way of doing this is to use all three of the entities (Function, realisedIn and NotProcess) as required connection points. We import no other ontologies here, so we can infer nothing that is not already stated.

 (defontology use-one) (defclass FunctionChild :subclass connection.function/Function (owl-some connection.function/realisedIn connection.upper/NotProcess))

In our second use, we now import our function ontology. At this point, the value of the shared identifier space starts to show its value; we now understand the semantics of our Function term because it uses the same identifier as the term in the function ontology.

This does, now, allow us to draw an additional inference; any individual of FunctionChild must be realisedIn an instance of NotProcess which, itself, we can infer to be a child of Process because the function ontology claims this. Or, in short, NotProcess and Process cannot be disjoint, if our ontology is to remain coherent. This ontology remains coherent, however, because we have not imported the upper ontology.

 (defontology use-two) (owl-import connection.function/function) ;; this ontology looks much the same as use-one (defclass FunctionChild :subclass connection.function/Function (owl-some connection.function/realisedIn connection.upper/NotProcess))

In the final use, we import both ontologies. The function import allows us to conclude that NotProcess and Process cannot be disjoint, while out upper ontology tells us that they are, and at this point, our ontology becomes incoherent. The required connection point in the function ontology has now been provided by term in our upper ontology.

 (defontology use-three) (owl-import connection.function/function) (owl-import connection.upper/upper) (defclass FunctionChild :subclass connection.function/Function (owl-some connection.function/realisedIn connection.upper/NotProcess))

The critical point is that while the function ontology references some term in its definition, the exact semantics of that term are not specified. These semantics are at the option of the downstream user of function ontology; in use-three, we have decided to fully specify these semantics. But we could have imported a totally different upper ontology had we chosen, either using the same identifiers, or through a bridge ontology making judicious use of equivalent/sameAs declarations. In short, the semantics has become late binding.

We can use this technique to improve on MIREOT. Instead of importing our derived ontology, we can now use connection points instead. The karyotype ontology can reference the NCBI taxonomy, and leave the end user to choose the semantics they need; if the user wants the whole taxonomy, and is prepared to deal with the reasoning speed, then have this option. This choice can even be made contextually; for example, an OWL import could be added on a continuous integration platform when reasoning time is less important, but not during development or interactive testing.

## Future Work

While the idea of connection points seems sound, it has some difficulties; one obvious problem is that the developer of an ontology must choose the modules, with connection points for themselves. We plan to test this using SIO; we have already been working on a tawnyified version of this, to enable investigation of pattern-driven ontology development. We will build on this work by attempting to modularise the ontology, with connection points between them.

Currently, the use of this form of connection points adds some load to the downstream ontology developer. It would be relatively easy for a developer to build an ontology like use-one or use-two above by mistake, accidentally forgetting to add an OWL import. Originally, when I built tawny, I wanted to automate this process — a Clojure import would mean an OWL import, but decided against it; obviously this was a good thing as it allows the use of connection points. I think we can work around this by adding formal support for connection points, so that for example, the function ontology can declare that nProcess needs to be defined somewhere, and to issue warnings if it it is not.

## Conclusions

In this post, I have addressed the problem of ontology modularity and described the use of connection points, enabling a form of late binding. In essence, we achieve this by building on OWLs web nature — shared identifiers do not presuppose shared semantics, in different ontologies. While further investigation is needed, this could change the nature of ontology engineering, allowing a more modular, more scalable and more pragmatic form of development.

## Acknowledgements

Thanks to Allyson Lister and James Malone for reviewing this article.

## Bibliography

### Kblog-Include

I have finally got around to releasing kblog-include, a plugin that I first alluded sometime previously . This plugin allows WordPress to transclude content from arXiv and potentially any OAI-PMH repository. When used from arXiv the date, authors and title are set (and advertised if kblog-metadata is installed also), and the abstract is added in place.

I’ve been using this for sometime now; in fact my last article is an example.

Feedback welcome, as always.

## 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.

### Tawny 0.12

I am pleased to announce the release of tawny-owl, Version 0.12.

This package allows users to construct OWL ontologies in a fully programmatic environment, namely Clojure. This means the user can take advantage of programmatic language to automate and abstract the ontology over the development process; also, rather than requiring the creation of ontology specific development environments, a normal programming IDE can be used; finally, a human readable text format means that we can integrate with the standard tooling for versioning and distributed development.

OWL is a W3C standard ontology representation language; an ontology is a fully computable set of statements, describing the things and their relationships. These statements can be reasoned over, inferences made and contradictions detected automatically using an off-the-shelf reasoner.

0.12 is planned to be the final, feature complete release before the 1.0 release. New features will not be added before 1.0. Key new features in 0.12 are:

• Complete support for OWL 2, include data types
• OWL documentation can be queries as normal clojure metadata
• New namespaces, query and fixture
• Completion of rendering functionality
• Regularisation of interfaces: where relevant functions now take an ontology as the first argument.
• Updated to Hermit 1.3.7.3, OWL API 3.4.5

Tawny is available at https://github.com/phillord/tawny-owl, or as a maven artifact from http://clojars.org. The development of tawny-owl is documented in my journal at http://www.russet.org.uk/blog/category/all/professional/tech/tawny-owl

### Data Properties in Tawny

Although it appears fairly innocuous, the last commit to tawny-owl seems momentus to me. While I still need to go through the spec line-by-line, and the code needs some clean up, this commit essentially represents the completion of the tawny.owl namespace; the addition of data properties and data types was the last part of the spec that I have to fulfil.

When I started off the tawny-owl library in October I was most interested in getting a test environment, and the ability to use a normal editor. Subsequently, and particularly in the course of writing up my first paper on this library , it became obvious to me that I needed to support all of OWL2. I think I have achieved my original design motivations and some more besides. I have also learned a lot about OWL, the OWL API and Manchester syntax. It is also a strange project, because it is the first time I have fulfilled a specification in quite this way. I cannot recall the last time I could reasonably be said to have finished something, as research is generally open-ended.

I did not, however, start with a regular syntax in mind. In general, the conversion to lisp has worked reasonably well: the object side of OWL in particular falls into a prefix, lisp syntax very naturally; the individual side less so. The data side of OWL had another surprise in store for me: it looks very similar to the object side; so I wanted to share syntax. However, all the Java method calls are named differently and take different types and parameter number.

In the end, I have supported this through a multi-method and some heuristics to guess which call is wanted. For instance, with these two calls from the pizza ontology:

 (owlsome hasTopping CheeseTopping) (owlsome hasCalorifiContentValue (span =< 400))

we generate quite different types of OWL object. The owlsome method defers to either object-some or data-some respectively, which can also be used directly. In this case, the difference is obvious; however, tawny also takes strings in most of these places; in this case, we convert to an IRI and check whether it exists in the ontology or any ontology we know about first. I suspect that these heuristics will work in most cases, but fail in some; only time and experience will tell me about these.

Before the next release, 0.12, I will finish both the inline, function documentation and update the tutorial. After this I plan to sit on the API a while, think about the functions and the syntax to make sure I am happy; the release after should be 1.0 and as is the way of these things, I will be stuck with the apperance of the API for quite a while. This also allows me to avoid a 0.13 release without accusation of superstition.

There are still many parts of tawny that I wish to improve; in particular, I need to extend the repl facilities with doc and apropos features — my attempt to hijack the Clojure native facilities have failed despite extensive efforts. And explanation code needs to go in; currently, waiting for protege to reason and produce these results in a soul destroying experience; I want me continuous integration tests to automatically dump explanations whenever inconsistencies happen.

But new features are for the future; for this iteration, tawny-owl is finished and now will be polished.