Building Ontologies in DAML + OIL

Comparative and Functional Genomics Comp Funct Genom 2003; 4: 133–141. Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/cfg.233 Conference Review Building ontologies in DAML + OIL

Robert Stevens*, Chris Wroe, Sean Bechhofer, Phillip Lord, Alan Rector and Carole Goble Department of Computer Science, University of Manchester, Oxford Road, Manchester M13 9PL, UK

*Correspondence to: Abstract Robert Stevens, Department of Computer Science, University of In this article we describe an approach to representing and building ontologies Manchester, Oxford Road, advocated by the Bioinformatics and Medical Informatics groups at the University Manchester M13 9PL, UK. of Manchester. The hand-crafting of ontologies offers an easy and rapid avenue to E-mail: delivering ontologies. Experience has shown that such approaches are unsustainable. [email protected] Description logic approaches have been shown to offer computational support for building sound, complete and logically consistent ontologies. A new knowledge representation language, DAML + OIL, offers a new standard that is able to support many styles of ontology, from hand-crafted to full logic-based descriptions with reasoning support. We describe this language, the OilEd editing tool, reasoning support and a strategy for the language’s use. We ﬁnish with a current example, in the Gene Ontology Next Generation (GONG) project, that uses DAML + OIL as the basis for moving the Gene Ontology from its current hand-crafted, form to one that uses logical descriptions of a concept’s properties to deliver a more complete version of the ontology. Copyright  2003 John Wiley & Sons, Ltd. Received: 11 November 2002 Keywords: description logic; ontology; DAML + OIL; ontology development envi- Accepted: 18 November 2002 ronment; ontology development methodology; reasoning

Introduction • TAMBIS [2] used a DL ontology to provide the illusion of a common query interface to multiple, In this article, we wish to present a style of diverse and distributed bioinformatics resources. building and delivering ontologies that we use in • MyGrid [3], which currently uses an ontology the Bioinformatics and Medical Informatics Groups of bioinformatics services to discover, compose at the University of Manchester. These groups have and semantically describe services available on been advocates for the use of ontologies in their the web. respective fields, and for associated technologies • The Gene Ontology Next Generation (GONG) for their building, and management. [4], which uses a DL to migrate the Gene Ontol- This article presents a view of ontologies built ogy (GO)[5] to an explicitly defined form that using a particular form of knowledge represen- delivers a more complete and robust ontology. tation language, viz. description logic (DL). The Medical Informatics and Bioinformatics groups Running through all these projects is the theme at the Manchester have a long history of build- of DLs as a knowledge representation language. ing and using ontologies in the biomedical fields, In this article we argue why we think this notably in: particular representation is good for describing biomedical knowledge and why one representation • The Galen [1] project, which demonstrated the in particular, DAML + OIL, is particularly well use of DLs and their associated compositional suited for this purpose. approach to descriptions to deliver large-scale Many of the resources used within biomedicine medical terminologies. contain not only data in the form of biological

Copyright  2003 John Wiley & Sons, Ltd. 134 R. Stevens et al. sequences, clinic attendance dates, temperatures, Thus, there is a need for a computational etc. but also act as vast knowledge repositories. approach to building ontologies. The approach we Much of this knowledge is stored in natural lan- offer can deliver such support, but it should not guage as scientific writing. The volume and com- exclude other, simpler approaches. Expert commu- plexity of this knowledge means that human sci- nities still need to be able to ‘buy in’ to the method entists increasingly need computational support to we advocate. manage and analyse these data and exploit this The particular knowledge representation lan- knowledge. As long ago as 1893, the British guage we use, DAML + OIL, does not exclude the medical establishment decided it needed a con- representation of simple, hand-crafted ontologies, trolled vocabulary describing ‘why people die’, so it is possible to migrate to full description-based in order to ease the generation of statistical data. ontologies, by increasing use of DAML + OIL’s So was born the International Classification of expressivity and reasoning support. As will be seen Diseases (ICD). ICD 10 [6] how contains over in this article, some problems still remain, but the 15 000 terms, held within a classification, rep- wide spectrum of styles supported by DAML + resenting diseases, symptoms, conditions, prob- OIL allows entry to this kind of technology at lems, complaints and other reasons for the provi- any level. sion of a medical service or procedure. Similarly, In this article we will describe the knowledge bioinformatics has seen the need for controlled representation language we use to describe our vocabularies, such as the SWISS-PROT keywords ontologies, an example of tool support for writ- (http://www.expasy.ch/cgi-bin/keywlist.pl)[8],to ing DAML + OIL and the way reasoning is used index its database entries and ease retrieval. Recent to support the process of building our ontolo- activities in bioinformatics, such as GO [5], have gies. We then present a general strategy to pro- seen the use of ontologies as a means of delivering vide the infrastructure for building an ontology controlled vocabularies. using a knowledge representation language such Traditionally, in these terminologies each con- as DAML + OIL. Next we introduce a project, cept or term has been placed by hand within GONG, where we use DAML + OIL to add con- the classification. Experience has shown that this cept descriptions for terms from GO and thus begin approach, while an easy and attractive method to the migration from a hand-crafted, phrase-based start with, is fraught with dangers [7]. In a classi- ontology to one couched in logical descriptions of fication where any particular term may have many those same concepts. Finally, we enter into a brief parents and many children, it is all too easy to omit discussion of the work at Manchester and some of parents or to place a new term incorrectly. Such our future activities. errors will leadnot only to an incorrect description of the domain, but to errors in recall and precision of queries and inaccurate statistics of phenomena. + If the hand-crafted approach has its drawbacks, Description logics and DAML OIL can the computer science community offer a better solution? Any solution needs to be usable by DAML + OIL is an ontology language specifi- biologists, who are by far the best people to build cally designed for the move from syntactic to full ontologies of biology. semantic interoperability of web-based resources The groups at Manchester would argue that DLs [9]. We can already plumb resources together eas- offer such a solution. This form of knowledge rep- ily enough but, as different resources use differ- resentation language does what the name suggests: ent values to represent the same knowledge and the ontology is described with logic expressions. the same values to represent different knowledge, A reasoning engine can then process these logi- this plumbing will not achieve the task of inter- cal expressions to produce a classification based operation. Such interoperability relies on machine- on those descriptions and to find any contradictions interpretable semantic descriptions. DAML + OIL in those descriptions. This means that, given suit- is underpinned by an expressive DL[10]. It is these able descriptions, a computer can build a taxonomy formal semantics that enable machine interpretation and place the concepts within that taxonomy at the and reasoning support and additionally aid human correct place, according to the descriptions given. communication–an aim of ontological description.

DAML + OIL takes an object-oriented approach DAML + OIL’s logical descriptions to work out to modelling, with the structure of the domain being the classification implied by those descriptions and described in terms of concepts or in DAML + any logical inconsistencies in those descriptions. OIL’s terms ‘classes’ and ‘properties’. Properties Hence the name ‘description logics’ — logics that are an explicit description of those attributes that reason about the descriptions of the class. enable class membership to be determined, e.g. a All this expressivity may be used in class hydrolase has the property of catalysing hydrolysis, descriptions and can be used to capture medi- whereas one of a transcription factor’s properties is cal, bioinformatics and molecular biology domain to bind to DNA. An DL ontology consists of a set knowledge with high fidelity. However, it is also of axioms that assert, for example, subsumption possible to simply assert class names within a tax- (‘kind-of’) relationships between classes or prop- onomic structure. Concept definitions can be as erties. So, we can state an axiom that says that the simple as possible yet as complex as necessary. + concept ‘enzyme’ is a subclass of the concept ‘pro- Thus, DAML OIL is capable of encoding a full tein’. We can also state an axiom that describes range of ontologies, but its power lies in the pos- the concept ‘enzyme’ as having the property of sibility of formal description and the reasoning it can then support [11]. ‘catalyses reaction’, i.e. a protein must catalyse a reaction in order to be an enzyme. Figure 1 shows a textual representation of a class or concept def- Building ontologies with oiled inition of some chemicals from the tricarboxylic acid cycle. A tricarboxylic acid is defined as a DAML + OIL provides us with a language for kind of organic acid that has three unionized car- defining ontologies. Expecting users to model boxylic or carboxylate anion groups. Oxaloacetate explicitly in DAML + OIL’s underlying RDF(S)- is described as having three such groups, but malate based concrete syntax is not a viable option, so in is described as only having two such groups. By order to fully exploit the expressiveness supported eye, we can reason that oxaloacetate is therefore by the language and encourage the use of the lan- a kind of tricarboxylic acid and malate is not. guage, tools are required that allow users to build With DAML + OIL, a machine reasoner can use and maintain these ontologies.

Figure 1. Some class descriptions for malate, oxaloacetate and tricarboxylic acid

OilEd (http://oiled.man.ac.uk) [11,12] is a sim- [including GONG (http://gong.man.ac.uk); ple ontology editor that allows users to con- [10,13] see Section 5] and myGrid (http://mygrid. struct and manipulate DAML + OIL ontologies man.ac.uk) [14], as a tool for teaching in several (Figure 2). It provides a graphical user interface university departments and for constructing ontolo- separating the user from the underlying concrete gies within industry. OilEd is freely available for syntax of DAML + OIL. In its current incarnation, download under an open source licence, and has OilEd is a relatively simple tool that does not sup- received over 1500 download requests to date. port the full range of functionality that would be expected of an ontology engineering environment (such as versioning, change management, support Reasoning for integration and merging). It does, however, sup- A key aspect of DAML + OIL is its well-defined port the full expressive power of DAML + OIL’s formal semantics (http://www.daml.org/2001/03/ concept language (note that OilEd’s datatype sup- model-theoretic-semantics.html) [15]. This pro- port is minimal) [10], allowing the user to build vides an account of exactly how we should inter- ontologies using the full range of constructors, pret composite expressions or descriptions in the such as Boolean operators and explicitly quantified language, and facilitates machine interpretation of restriction types. The user is not required to model DAML + OIL ontologies. For example, DAML + using complex constructs, though, and the tool can OIL contains different restriction types representing be used to construct simple taxonomies, which may existential and universal quantification, allowing us then be further elaborated at a later date. to be explicit about representing, for example, the In spite of its limitations, OilEd has been used class of proteins which can bind to some DNA but successfully in a number of academic projects can also bind to other things, or alternatively, the

Figure 2. OilEd

Copyright  2003 John Wiley & Sons, Ltd. Comp Funct Genom 2003; 4: 133–141. Building ontologies in DAML + OIL 137 class of proteins which only bind to DNA and not Just because we describe our concepts using to other things. logic expressions does not in itself deliver ‘good’ These semantics then allow us to employ rea- ontologies. It is a truism that logic guarantees soners to infer relationships between classes. In only that truth follows from truth. Logic says particular, we can infer subsumption, or ‘kind-of’ nothing about what follows from falsehood, neither links between class descriptions, and thus build can a logic engine make deductions from any classification hierarchies which are based precisely information unless it is explicitly represented. This on the semantics of the descriptions applied to means that to use logic you must tell ‘the truth, classes. In addition, satisfiability testing allows us the whole truth, and nothing but the truth’. DL- to determine when class definitions are unsatisfi- based ontologies always cover wider ground than able or incoherent, i.e. when no instances of the the application to which they will be put. In order class could possibly exist. to describe enzymes, we also need to describe OilEd Uses a reasoner [the current version uses reactions, substrates, products and co-factors. The the FaCT (http://www.cs.man.ac.uk/fact) [16] DL ‘whole truth’ is always bigger than any model. All reasoner] to organize concept hierarchies. The models, even logical models, are approximations. ontology is translated to an equivalent DL model, There are two commonly occurring problems: (a) and the reasoner is then used to build a classifica- not telling the truth; and (b) not telling the whole tion hierarchy of the concepts in the model. truth. Not telling the truth — lying to the sys- This use of a reasoner is a useful addition to tem — happens most frequently when the system the ontologist’s toolbox, particularly during the is not expressive enough to say what we want to construction and maintenance of ontologies. The say. This is less of a problem with DAML + OIL, task of ontology integration can also be supported as it has a great expressivity when compared with with reasoning — cross-ontology relationships can previous representations. We are, for example, able be defined, with the reasoner assisting in spotting to say that a GPCR must have only seven trans- equivalences between concept definitions in differ- membrane regions, but having seven such regions ent ontologies. This is an approach that has proved does not necessarily make something a GPCR. successful in the context of integrating database Despite this expressivity, ‘lying’ still occurs. The schemas (e.g. ICOM [17]). other reason for not telling the truth is blunder and confusion. A more expressive language makes this, if anything, more likely because there are many Normalization and modularization of ways to say any one thing, and the consequences of ontologies ‘clever’ solutions can be hard to determine. So the first purpose of normalization is to ‘keep it simple’. So, we have an expressive knowledge representa- The second problem, not telling the whole truth, tion language, an editor for building an ontology is much more difficult to manage. It is extremely and the possibility of using machine support for easy to leave out information or, even more per- reasoning with such an ontology. We have already nicious, to represent it only in the names, com- described how handcrafting ontologies can become ments, and perhaps conventions. It is obvious to difficult as they become large and complex. We any reader why ‘protease’ falls under both ‘protein’ offer a way of avoiding such problems, but at the and ‘enzyme’, that one hierarchical link has to do cost of adding further complexity. We need guide- with structure, the other with function. But how is lines on how to use such representations and sup- this to be represented to a logic engine? What if we port. A logical mess is no better than a hand-crafted want to take out one structure ontology and plug in mess. Figure 1 shows the kind of complexity that another, new, improved version? What if we want may arise in a DAML + OIL ontology. We need to extend the detail of protein structure and keep to avoid unmanageable complexity and make sure the same structure of biological function, or vice our descriptions are not ‘tangled’; we do this by versa? Normalization provides a means of distin- normalizing the parts of our ontology into sim- guishing the reasons for classification and imposes ple modules that can then be combined to give the a discipline that makes it much more likely that complex descriptions in which we are interested. authors will make all relevant information explicit.

At the same time, normalization makes it possi- and so allow the curators to focus on curating ble to take ontologies apart into pieces and put the biological knowledge. Providing a detailed them back together again. To do this logically, the conceptualization of every GO concept is a large ‘joins’ have to be explicit and the sections must task, and so GONG aims to take a staged approach. not overlap. Small inroads will be taken in order to solve What normalization does is to turn all multi- specific problems. As more formal definitions are ple classification over to the logic engine. Then, produced, the reasoner can be used more frequently if the reason for classification is not explicit, the to give support to the manual curation task. classification does not happen — which is usually The first specific task for GONG is helping to easy to spot and fix. A minimal skeleton of sim- maintain the metabolism section of the existing ple trees is still required — you have to start some GO. Within GO many concepts have multiple par- where — but for everything else sufficient infor- ents. That is because many concepts can be sensi- mation must be represented so that the classifier bly grouped in more than one manner. The main- can make the correct inferences. tenance of ‘this-is-a’ links is a manual process. So we transform the process of developing a complex richly interconnected multiple hierarchy Experience from the medical domain has shown into the much simpler task of creating many that numerous parent–child links are omitted in smaller, simpler hierarchies. Each simple hierarchy such hand-crafted, phrase-based controlled vocab- has to be a simple tree, i.e. each concept in the ularies [7]. While of less importance to manual skeleton has only one parent in the skeleton. Then interpretation, machine interpretation will falter in we provide the descriptions and definitions that the face of such inconsistencies. links the simple hierarchies and let the logic engine Take, for example, the metabolism concepts. The infer the complex structure. If something is missing majority of metabolism concepts within the GO in the definitions or descriptions, it is usually the describe a metabolic process acting on a chemi- case that some concept appears grossly out of place, cal molecule, e.g. heparin biosynthesis. This con- usually much too far up the classification, where it cept can be grouped by the nature of the pro- stands out. If something is inconsistent, then the cess; heparin biosynthesis is a kind of heparin reasons for the inconsistency will be explicit and metabolism because biosynthesis is a particular the logic engine will mark it. The result is a simple, kind of metabolism. The concept can also be consistent structure which we can explain to others, grouped by the nature of the chemical being pro- maintain and extend. cessed; heparin biosynthesis is a kind of peptido- glycan biosynthesis, because heparin belongs to the class of peptidoglycans. Figure 3 shows how the Migrating ontologies to a property-based concept heparin biosynthesis was organized in the form: Gong July 2002 version of GO. However, additional parent relationships are We can see an instance of this normalization possible and their inclusion does affect the retrieval in the GONG project, where DAML + OIL is of information using the GO. In fact, heparin being used to give reasoning-based support to the development of GO. To make descriptions of is more specifically a kind of glycosaminogly- GO terms we must, for example, add ontologies can, and so heparin biosynthesis could sensibly be of chemicals in order to make descriptions of placed as a kind of glycosaminoglycan biosynthe- enzyme and metabolism terms. The growing size sis. Addition of ‘this-is-a’ link now allows extra and complexity of GO is forcing its curators to gene products to be returned from a query ask- spend more and more time on the mundane task of ing for all gene products that have the biological maintaining the consistency and completeness of process concept ‘glycosaminoglycan biosynthesis’, its internal structure. The GONG project aims to because by extension they will include gene prod- demonstrate that, in principle, migrating to a finer- ucts annotated with the process concept ‘heparin grained formal conceptualization in DAML + OIL biosynthesis’. will allow computational techniques such as DLs How can we use DL to maintain the links auto- to aid in the curation and delivery of the ontology matically? First, we dissect the concept, explicitly

is-a biological process

is-a cell growth and/or maintenance

is-a metabolism

is-a carbohydrate metabolism

is-a aminoglycan metabolism

is-a glycosaminoglycan metabolism is-a heparin metabolism

is-a heparin biosynthesis

Figure 3. Directed acyclic graph showing position of heparin biosynthesis in the original Gene Ontology (July 2002) stating the concept’s definition in a formal repre- The process of dissection breaks down the exist- sentation. This provides the substrate for DL rea- ing concept into more elemental concepts related soners to infer new ‘is-a’ links and remove redun- together in a formal semantic manner. These dant links. Within a large phrased-based ontology, elemental concepts rarely exist in the original such as GO, which contains many concepts within ontology and so themselves have to be defined. a narrow semantic range, it is possible to use auto- These are the hand-crafted, single axial taxonomies mated techniques to construct candidate dissections described in the normalization stage in the section by simply parsing the term names. on Normalization and Modularization of ontoge- For example, many metabolism terms in the nies, above. The nature of these elemental defini- GO follow the pattern, ‘chemical name followed tions can range from a simple taxonomy to complex by either metabolism or catabolism or biosynthe- dissections in their own right. Knowledge is fractal, sis’. If a term name fits this pattern, a dissec- and the decision about ‘how far down to model’ is tion can be created from the relevant phrase con- based on the degree of knowledge the final ontol- stituents. Table 1 shows the DAML + OIL defini- ogy needs to support. For example, GO is not used tions for heparin biosynthesis and glycosaminogly- to annotate information concerning detailed chem- can biosynthesis. ical atomic substructure, and so modelling that

Table 1. Human readable DAML + OIL deﬁnitions for heparin biosynthesis and glycosaminoglycan biosynthesis

Concept name heparin biosynthesis

class heparin biosynthesis deﬁned DAML + OIL subClassOf biosynthesis deﬁnition restriction onProperty acts−on hasClass heparin (acts−on is a unique property) Paraphrase biosynthesis which acts solely on heparin

Concept name glycosaminoglycan biosynthesis

class glycosaminoglycan biosynthesis deﬁned DAML + OIL subClassOf biosynthesis deﬁnition restriction onProperty acts−on hasClass glycosaminoglycan Paraphrase biosynthesis which acts solely on glycosaminoglycans

is-a biological process

is-a cell growth and/or maintenance

is-a metabolism

is-a carbohydrate metabolism

is-a aminoglycan metabolism

is-a glycosaminoglycan metabolism is-a heparin metabolism

is-a heparin biosynthesis

is-a glycosaminoglycan biosynthesis

is-a heparin biosynthesis

Figure 4. Directed acyclic graph showing additional parent for heparin biosynthesis found using the reasoner knowledge would be fruitless at this stage. Once all a child of the more specific term, ‘amino acid the explicit terminological knowledge is in place, derivative catabolism; GO0042219’ instead. The we can submit it to the reasoner and examine the editor then created a new term, ‘taurine biosyn- report of the new subsumption links it has inferred, thesis; GO0042412’ and made it a child of both what links are redundant, and what concept defini- ‘taurine metabolism; GO0019530’ and ‘amino acid tions are logically inconsistent. derivative biosynthesis; GO0042398’. Reassuringly, the changes reported by the DL Although the GONG project has only tackled reasoner represent mostly additional relationships a small portion of GO, it has shown the utility hard to spot by human eye, and not errors in of such an approach. In our first experiment, we biological knowledge. migrated 350 metabolism terms to the DAML + For example, the reasoner reported that heparin OIL, property-based form. From these descriptions, biosynthesis has a new ‘is-a’ parent, ‘glycosamino- 22 missing ‘is-a’ relationships were found. This glycan biosynthesis’. Figure 4 shows the hierar- means that nearly one in ten concepts in the chy that results from these descriptions being sub- hand-crafted form of GO were not completely mitted to the reasoner; it can be compared with described. This does not mean that GO is bad or Figure 3. These reports can then be sent to the wrong — it is another demonstration that building editorial team for comment and action if neces- ontologies is difficult. As already mentioned, few sary. Although the nature of the changes made by real biological errors have been found — most the reasoner are limited and may not be accepted were errors of omission. We feel that we have by the GO team as the correct solution, in terms already begun to show the utility of an approach of the biology, they are helpful in pointing out in which it is possible to use one knowledge areas of the ontology which may need attention. representation language to both represent a hand- For example, inferring a new subsumption rela- crafted ontology and migrate it in situ to a more tionship to a very general concept may point out expressive, property-based form. the need for new ontology fragments incorpo- rating new intermediate concepts. The reasoner Discussion reported that ‘taurine catabolism’ GO0019529 has new super ‘catabolism’ GO0009056. This led to In this article we have presented an approach to the GO editor to actually make ‘taurine catabolism’ representing ontologies used in the Bioinformatics

Copyright  2003 John Wiley & Sons, Ltd. Comp Funct Genom 2003; 4: 133–141. Building ontologies in DAML + OIL 141 and Medical Informatics groups at the University supported by EU Grant IST-2001-33052. We would like to of Mancherster. We are now using DAML + OIL thank Midori Harris and Jane Lomax of the GO editorial as our knowledge representation language. This team for their advice and for responding to the reports. allows us to use ontologies represented in a variety of forms, from hand-crafted taxonomies of phrases to full-blown concept definitions based upon the References use of a concept’s properties. In the latter form, DL-based reasoners can be used to infer the classi- 1. Rogers J, Roberts A, Solomon D, et al. 2001. GALEN ten fication encoded within the descriptions of a con- years on: tasks and supporting tools. In MedInfo 2001 Studies cept’s properties. in Health Technology and Informatics, Haux R, Rogers R, Such a DAML + OIL-based representation Patel V (eds). IOS Press: Amsterdam; 256–260. allows ontologies to be as simple as possible, 2. Goble CA, Stevens R, Ng G, et al. 2001. Transparent Access yet as complex as necessary. In addition, we can to Multiple Bioinformatics Information Sources. IBM Systems, migrate from the simpler form to the more com- (special issue on deep computing for the life sciences) 40(2): 532–552. plex, reasoning-supported form without having to 3. Wroe C, Stevens RD, Goble CA, Roberts A, Greenwood M. throw away the simpler representation. Due to this 2003. A suite of DAML + OIL Ontologies to describe range of entry levels, it is possible to include bioinformatics services and data. Int J Coop Inf Syst (accepted experts in the domain, rather than in DL’s, in the for publication). process of constructing DAML + OIL ontologies, 4. Wroe CJ, Stevens RD, Goble CA, Ashburner M. A methodology to migrate the Gene Ontology to a description logic allowing exploitation of their specialist knowledge. environment using DAML + OIL. 8th Pacific Symposium on Although with tools such as OilEd and the rea- Biocomputing (PSB) (accepted for presentation, 2003). soners it encompasses, we have the foundations 5. The Gene Ontology Consortium. 2000. Gene Ontology: tool of a full ontology development environment and for the unification of biology. Nature Genet 25: 25–29. methodology, much work remains to be done. As 6. World Health Organization. 1992. International Statistical Classification of Diseases and Related Health Problems, 10th well as the extensions needed for OilEd described revision (ICD-10). World Health Organization: Geneva. above, some barriers still remain to non-specialist 7. Rogers JE, Price C, Rector AL, Solomon WD, Smejko N. use of DAML + OIL. Using the full expressivity 1998. Validating clinical terminology structures: integration of DAML + OIL is still difficult, particularly the and cross-validation of Read Thesaurus and GALEN. In AMIA use of elements such as universal and existential Fall Symposium, Lake Buena Vista, FL, USA. 8. Swiss-PROT Keywords: http://www.expasy.ch/cgi-bin/ quantification. GONG has begun to develop meth- keywlist.pL ods and tools to support the migration of terms 9. Baader F, McGuinness D, Nardi D, Schneider PP (eds). 2003. to property descriptions through automated dissec- The Description Logic Handbook: Theory, Implementation and tions. However, techniques that will allow high- Applications. Cambridge University Press: Cambridge. expressivity not to be a barrier between expert and 10. Horrocks I. 2002. DAML + OIL: a reason-able web ontology representation still need to be developed and are language. In Proc. EDBT 2002. Lecture Notes in Computer Science. Springer-Verlag, Heidelberg, 2–13. an active area of research. Nevertheless, we feel 11. Stevens R, Horrocks I, Goble C, Bechhofer S. 2001. Building that the approach advocated at Manchester forms a reasonable bioinformatics ontology using OIL. In the basis for moving arguments from ‘What is an Proceedings of the IJCAI 2001 Workshop on Ontologies and ontology?’ to ‘How do we best deliver ontologies Information Sharing, 81–90. that serve the purposes we require?’ 12. OilEd: http://oiled.man.ac.uk 13. GONG: http://gong.man.ac.uk 14. myGrid: http://mygrid.man.ac.uk Acknowledgements 15. DAML + OIL semantics: http://www.damL.org/2001/03/ model-theoretic-semantics.htmL Chris Wroe is supported by the myGrid e-Science pilot 16. FaCT: http://www.cs.man.ac.uk/fact (EPSRC GR/R67743) and GONG (DARPA DAML sub- 17. Franconi E, Ng G. 2000. The i.com tool for intelligent contract PY-1149 from Stanford University). Phil Lord is conceptual modelling. In 7th Intl. Workshop on Knowledge also supported by the myGrid project. Sean Bechhofer is Representation meets Databases (KRDB’00).

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