building ontologies in daml + oil

Comparative and Functional GenomicsComp 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 GobleDepartment of Computer Science, University of Manchester, Oxford Road, Manchester M13 9PL, UK

*Correspondence to:Robert Stevens, Department ofComputer Science, University ofManchester, Oxford Road,Manchester M13 9PL, UK.E-mail:[email protected]

Received: 11 November 2002Accepted: 18 November 2002

AbstractIn this article we describe an approach to representing and building ontologiesadvocated by the Bioinformatics and Medical Informatics groups at the Universityof Manchester. The hand-crafting of ontologies offers an easy and rapid avenue todelivering ontologies. Experience has shown that such approaches are unsustainable.Description logic approaches have been shown to offer computational support forbuilding sound, complete and logically consistent ontologies. A new knowledgerepresentation language, DAML + OIL, offers a new standard that is able to supportmany styles of ontology, from hand-crafted to full logic-based descriptions withreasoning support. We describe this language, the OilEd editing tool, reasoningsupport 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 asthe basis for moving the Gene Ontology from its current hand-crafted, form to onethat uses logical descriptions of a concept’s properties to deliver a more completeversion of the ontology. Copyright 2003 John Wiley & Sons, Ltd.

Keywords: description logic; ontology; DAML + OIL; ontology development envi-ronment; ontology development methodology; reasoning

Introduction

In this article, we wish to present a style ofbuilding and delivering ontologies that we use inthe Bioinformatics and Medical Informatics Groupsat the University of Manchester. These groups havebeen advocates for the use of ontologies in theirrespective fields, and for associated technologiesfor their building, and management.

This article presents a view of ontologies builtusing a particular form of knowledge represen-tation language, viz. description logic (DL). TheMedical Informatics and Bioinformatics groupsat the Manchester have a long history of build-ing and using ontologies in the biomedical fields,notably in:

• The Galen [1] project, which demonstrated theuse of DLs and their associated compositionalapproach to descriptions to deliver large-scalemedical terminologies.

• TAMBIS [2] used a DL ontology to provide theillusion of a common query interface to multiple,diverse and distributed bioinformatics resources.

• MyGrid [3], which currently uses an ontologyof bioinformatics services to discover, composeand semantically describe services available onthe web.

• The Gene Ontology Next Generation (GONG)[4], which uses a DL to migrate the Gene Ontol-ogy (GO)[5] to an explicitly defined form thatdelivers a more complete and robust ontology.

Running through all these projects is the themeof DLs as a knowledge representation language.In this article we argue why we think thisparticular representation is good for describingbiomedical knowledge and why one representationin particular, DAML + OIL, is particularly wellsuited for this purpose.

Many of the resources used within biomedicinecontain not only data in the form of biological

Copyright 2003 John Wiley & Sons, Ltd.

134 R. Stevens et al.

sequences, clinic attendance dates, temperatures,etc. but also act as vast knowledge repositories.Much of this knowledge is stored in natural lan-guage as scientific writing. The volume and com-plexity of this knowledge means that human sci-entists increasingly need computational support tomanage and analyse these data and exploit thisknowledge. As long ago as 1893, the Britishmedical establishment decided it needed a con-trolled vocabulary describing ‘why people die’,in order to ease the generation of statistical data.So was born the International Classification ofDiseases (ICD). ICD 10 [6] how contains over15 000 terms, held within a classification, rep-resenting diseases, symptoms, conditions, prob-lems, complaints and other reasons for the provi-sion of a medical service or procedure. Similarly,bioinformatics has seen the need for controlledvocabularies, such as the SWISS-PROT keywords(http://www.expasy.ch/cgi-bin/keywlist.pl) [8], toindex its database entries and ease retrieval. Recentactivities in bioinformatics, such as GO [5], haveseen the use of ontologies as a means of deliveringcontrolled vocabularies.

Traditionally, in these terminologies each con-cept or term has been placed by hand withinthe classification. Experience has shown that thisapproach, while an easy and attractive method tostart with, is fraught with dangers [7]. In a classi-fication where any particular term may have manyparents and many children, it is all too easy to omitparents or to place a new term incorrectly. Sucherrors will leadnot only to an incorrect descriptionof the domain, but to errors in recall and precisionof queries and inaccurate statistics of phenomena.

If the hand-crafted approach has its drawbacks,can the computer science community offer a bettersolution? Any solution needs to be usable bybiologists, who are by far the best people to buildontologies of biology.

The groups at Manchester would argue that DLsoffer such a solution. This form of knowledge rep-resentation language does what the name suggests:the ontology is described with logic expressions.A reasoning engine can then process these logi-cal expressions to produce a classification basedon those descriptions and to find any contradictionsin those descriptions. This means that, given suit-able descriptions, a computer can build a taxonomyand place the concepts within that taxonomy at thecorrect place, according to the descriptions given.

Thus, there is a need for a computationalapproach to building ontologies. The approach weoffer can deliver such support, but it should notexclude other, simpler approaches. Expert commu-nities still need to be able to ‘buy in’ to the methodwe advocate.

The particular knowledge representation lan-guage we use, DAML + OIL, does not exclude therepresentation of simple, hand-crafted ontologies,so it is possible to migrate to full description-basedontologies, by increasing use of DAML + OIL’sexpressivity and reasoning support. As will be seenin this article, some problems still remain, but thewide spectrum of styles supported by DAML +OIL allows entry to this kind of technology atany level.

In this article we will describe the knowledgerepresentation language we use to describe ourontologies, an example of tool support for writ-ing DAML + OIL and the way reasoning is usedto support the process of building our ontolo-gies. We then present a general strategy to pro-vide the infrastructure for building an ontologyusing a knowledge representation language suchas DAML + OIL. Next we introduce a project,GONG, where we use DAML + OIL to add con-cept descriptions for terms from GO and thus beginthe migration from a hand-crafted, phrase-basedontology to one couched in logical descriptions ofthose same concepts. Finally, we enter into a briefdiscussion of the work at Manchester and some ofour future activities.

Description logics and DAML + OIL

DAML + OIL is an ontology language specifi-cally designed for the move from syntactic to fullsemantic interoperability of web-based resources[9]. We can already plumb resources together eas-ily enough but, as different resources use differ-ent values to represent the same knowledge andthe same values to represent different knowledge,this plumbing will not achieve the task of inter-operation. Such interoperability relies on machine-interpretable semantic descriptions. DAML + OILis underpinned by an expressive DL[10]. It is theseformal semantics that enable machine interpretationand reasoning support and additionally aid humancommunication–an aim of ontological description.

Copyright 2003 John Wiley & Sons, Ltd. Comp Funct Genom 2003; 4: 133–141.

Building ontologies in DAML + OIL 135

DAML + OIL takes an object-oriented approachto modelling, with the structure of the domain beingdescribed in terms of concepts or in DAML +OIL’s terms ‘classes’ and ‘properties’. Propertiesare an explicit description of those attributes thatenable class membership to be determined, e.g. ahydrolase has the property of catalysing hydrolysis,whereas one of a transcription factor’s properties isto bind to DNA. An DL ontology consists of a setof axioms that assert, for example, subsumption(‘kind-of’) relationships between classes or prop-erties. So, we can state an axiom that says that theconcept ‘enzyme’ is a subclass of the concept ‘pro-tein’. We can also state an axiom that describesthe concept ‘enzyme’ as having the property of‘catalyses reaction’, i.e. a protein must catalyse areaction in order to be an enzyme. Figure 1 showsa textual representation of a class or concept def-inition of some chemicals from the tricarboxylicacid cycle. A tricarboxylic acid is defined as akind of organic acid that has three unionized car-boxylic or carboxylate anion groups. Oxaloacetateis described as having three such groups, but malateis described as only having two such groups. Byeye, we can reason that oxaloacetate is thereforea kind of tricarboxylic acid and malate is not.With DAML + OIL, a machine reasoner can use

DAML + OIL’s logical descriptions to work outthe classification implied by those descriptions andany logical inconsistencies in those descriptions.Hence the name ‘description logics’ — logics thatreason about the descriptions of the class.

All this expressivity may be used in classdescriptions and can be used to capture medi-cal, bioinformatics and molecular biology domainknowledge with high fidelity. However, it is alsopossible to simply assert class names within a tax-onomic structure. Concept definitions can be assimple as possible yet as complex as necessary.Thus, DAML + OIL is capable of encoding a fullrange of ontologies, but its power lies in the pos-sibility of formal description and the reasoning itcan then support [11].

Building ontologies with oiled

DAML + OIL provides us with a language fordefining ontologies. Expecting users to modelexplicitly in DAML + OIL’s underlying RDF(S)-based concrete syntax is not a viable option, so inorder to fully exploit the expressiveness supportedby the language and encourage the use of the lan-guage, tools are required that allow users to buildand 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-ple ontology editor that allows users to con-struct and manipulate DAML + OIL ontologies(Figure 2). It provides a graphical user interfaceseparating the user from the underlying concretesyntax of DAML + OIL. In its current incarnation,OilEd is a relatively simple tool that does not sup-port the full range of functionality that would beexpected of an ontology engineering environment(such as versioning, change management, supportfor integration and merging). It does, however, sup-port the full expressive power of DAML + OIL’sconcept language (note that OilEd’s datatype sup-port is minimal) [10], allowing the user to buildontologies using the full range of constructors,such as Boolean operators and explicitly quantifiedrestriction types. The user is not required to modelusing complex constructs, though, and the tool canbe used to construct simple taxonomies, which maythen be further elaborated at a later date.

In spite of its limitations, OilEd has been usedsuccessfully in a number of academic projects

[including GONG (http://gong.man.ac.uk);[10,13] see Section 5] and myGrid (http://mygrid.man.ac.uk) [14], as a tool for teaching in severaluniversity departments and for constructing ontolo-gies within industry. OilEd is freely available fordownload under an open source licence, and hasreceived over 1500 download requests to date.

Reasoning

A key aspect of DAML + OIL is its well-definedformal semantics (http://www.daml.org/2001/03/model-theoretic-semantics.html) [15]. This pro-vides an account of exactly how we should inter-pret composite expressions or descriptions in thelanguage, and facilitates machine interpretation ofDAML + OIL ontologies. For example, DAML +OIL contains different restriction types representingexistential and universal quantification, allowing usto be explicit about representing, for example, theclass of proteins which can bind to some DNA butcan also bind to other things, or alternatively, the

Figure 2. OilEd



class of proteins which only bind to DNA and notto other things.

These semantics then allow us to employ rea-soners to infer relationships between classes. Inparticular, we can infer subsumption, or ‘kind-of’links between class descriptions, and thus buildclassification hierarchies which are based preciselyon the semantics of the descriptions applied toclasses. In addition, satisfiability testing allows usto determine when class definitions are unsatisfi-able or incoherent, i.e. when no instances of theclass could possibly exist.

OilEd Uses a reasoner [the current version usesthe FaCT (http://www.cs.man.ac.uk/fact) [16] DLreasoner] to organize concept hierarchies. Theontology is translated to an equivalent DL model,and the reasoner is then used to build a classifica-tion hierarchy of the concepts in the model.

This use of a reasoner is a useful addition tothe ontologist’s toolbox, particularly during theconstruction and maintenance of ontologies. Thetask of ontology integration can also be supportedwith reasoning — cross-ontology relationships canbe defined, with the reasoner assisting in spottingequivalences between concept definitions in differ-ent ontologies. This is an approach that has provedsuccessful in the context of integrating databaseschemas (e.g. ICOM [17]).

Normalization and modularization ofontologies

So, we have an expressive knowledge representa-tion language, an editor for building an ontologyand the possibility of using machine support forreasoning with such an ontology. We have alreadydescribed how handcrafting ontologies can becomedifficult as they become large and complex. Weoffer a way of avoiding such problems, but at thecost of adding further complexity. We need guide-lines on how to use such representations and sup-port. A logical mess is no better than a hand-craftedmess. Figure 1 shows the kind of complexity thatmay arise in a DAML + OIL ontology. We needto avoid unmanageable complexity and make sureour descriptions are not ‘tangled’; we do this bynormalizing the parts of our ontology into sim-ple modules that can then be combined to give thecomplex descriptions in which we are interested.

Just because we describe our concepts usinglogic expressions does not in itself deliver ‘good’ontologies. It is a truism that logic guaranteesonly that truth follows from truth. Logic saysnothing about what follows from falsehood, neithercan a logic engine make deductions from anyinformation unless it is explicitly represented. Thismeans that to use logic you must tell ‘the truth,the whole truth, and nothing but the truth’. DL-based ontologies always cover wider ground thanthe application to which they will be put. In orderto describe enzymes, we also need to describereactions, substrates, products and co-factors. The‘whole truth’ is always bigger than any model. Allmodels, even logical models, are approximations.

There are two commonly occurring problems: (a)not telling the truth; and (b) not telling the wholetruth. Not telling the truth — lying to the sys-tem — happens most frequently when the systemis not expressive enough to say what we want tosay. This is less of a problem with DAML + OIL,as it has a great expressivity when compared withprevious representations. We are, for example, ableto say that a GPCR must have only seven trans-membrane regions, but having seven such regionsdoes not necessarily make something a GPCR.Despite this expressivity, ‘lying’ still occurs. Theother reason for not telling the truth is blunder andconfusion. A more expressive language makes this,if anything, more likely because there are manyways to say any one thing, and the consequences of‘clever’ solutions can be hard to determine. So thefirst purpose of normalization is to ‘keep it simple’.

The second problem, not telling the whole truth,is much more difficult to manage. It is extremelyeasy to leave out information or, even more per-nicious, to represent it only in the names, com-ments, and perhaps conventions. It is obvious toany reader why ‘protease’ falls under both ‘protein’and ‘enzyme’, that one hierarchical link has to dowith structure, the other with function. But how isthis to be represented to a logic engine? What if wewant to take out one structure ontology and plug inanother, new, improved version? What if we wantto extend the detail of protein structure and keepthe same structure of biological function, or viceversa? Normalization provides a means of distin-guishing the reasons for classification and imposesa discipline that makes it much more likely thatauthors will make all relevant information explicit.



At the same time, normalization makes it possi-ble to take ontologies apart into pieces and putthem back together again. To do this logically, the‘joins’ have to be explicit and the sections mustnot overlap.

What normalization does is to turn all multi-ple classification over to the logic engine. Then,if the reason for classification is not explicit, theclassification does not happen — which is usuallyeasy to spot and fix. A minimal skeleton of sim-ple trees is still required — you have to start somewhere — but for everything else sufficient infor-mation must be represented so that the classifiercan make the correct inferences.

So we transform the process of developing acomplex richly interconnected multiple hierarchyinto the much simpler task of creating manysmaller, simpler hierarchies. Each simple hierarchyhas to be a simple tree, i.e. each concept in theskeleton has only one parent in the skeleton. Thenwe provide the descriptions and definitions thatlinks the simple hierarchies and let the logic engineinfer the complex structure. If something is missingin the definitions or descriptions, it is usually thecase that some concept appears grossly out of place,usually much too far up the classification, where itstands out. If something is inconsistent, then thereasons for the inconsistency will be explicit andthe logic engine will mark it. The result is a simple,consistent structure which we can explain to others,maintain and extend.

Migrating ontologies to a property-basedform: Gong

We can see an instance of this normalizationin the GONG project, where DAML + OIL isbeing used to give reasoning-based support tothe development of GO. To make descriptions ofGO terms we must, for example, add ontologiesof chemicals in order to make descriptions ofenzyme and metabolism terms. The growing sizeand complexity of GO is forcing its curators tospend more and more time on the mundane task ofmaintaining the consistency and completeness ofits internal structure. The GONG project aims todemonstrate that, in principle, migrating to a finer-grained formal conceptualization in DAML + OILwill allow computational techniques such as DLsto aid in the curation and delivery of the ontology

and so allow the curators to focus on curatingthe biological knowledge. Providing a detailedconceptualization of every GO concept is a largetask, and so GONG aims to take a staged approach.Small inroads will be taken in order to solvespecific problems. As more formal definitions areproduced, the reasoner can be used more frequentlyto give support to the manual curation task.

The first specific task for GONG is helping tomaintain the metabolism section of the existingGO. Within GO many concepts have multiple par-ents. That is because many concepts can be sensi-bly grouped in more than one manner. The main-tenance of ‘this-is-a’ links is a manual process.Experience from the medical domain has shownthat numerous parent–child links are omitted insuch hand-crafted, phrase-based controlled vocab-ularies [7]. While of less importance to manualinterpretation, machine interpretation will falter inthe face of such inconsistencies.

Take, for example, the metabolism concepts. Themajority of metabolism concepts within the GOdescribe a metabolic process acting on a chemi-cal molecule, e.g. heparin biosynthesis. This con-cept can be grouped by the nature of the pro-cess; heparin biosynthesis is a kind of heparinmetabolism because biosynthesis is a particularkind of metabolism. The concept can also begrouped by the nature of the chemical being pro-cessed; heparin biosynthesis is a kind of peptido-glycan biosynthesis, because heparin belongs to theclass of peptidoglycans. Figure 3 shows how theconcept heparin biosynthesis was organized in theJuly 2002 version of GO.

However, additional parent relationships arepossible and their inclusion does affect the retrievalof information using the GO. In fact, heparinis more specifically a kind of glycosaminogly-can, and so heparin biosynthesis could sensibly beplaced as a kind of glycosaminoglycan biosynthe-sis. Addition of ‘this-is-a’ link now allows extragene products to be returned from a query ask-ing for all gene products that have the biologicalprocess concept ‘glycosaminoglycan biosynthesis’,because by extension they will include gene prod-ucts annotated with the process concept ‘heparinbiosynthesis’.

How can we use DL to maintain the links auto-matically? First, we dissect the concept, explicitly



is-a cell growth and/or maintenance

metabolism

heparin biosynthesis

carbohydrate metabolism

aminoglycan metabolism

biological processis-a

is-a

is-a

is-a

is-a

is-a

is-a

glycosaminoglycan metabolism

heparin metabolism

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-sentation. This provides the substrate for DL rea-soners to infer new ‘is-a’ links and remove redun-dant links. Within a large phrased-based ontology,such as GO, which contains many concepts withina narrow semantic range, it is possible to use auto-mated techniques to construct candidate dissectionsby simply parsing the term names.

For example, many metabolism terms in theGO follow the pattern, ‘chemical name followedby either metabolism or catabolism or biosynthe-sis’. If a term name fits this pattern, a dissec-tion can be created from the relevant phrase con-stituents. Table 1 shows the DAML + OIL defini-tions for heparin biosynthesis and glycosaminogly-can biosynthesis.

The process of dissection breaks down the exist-ing concept into more elemental concepts relatedtogether in a formal semantic manner. Theseelemental concepts rarely exist in the originalontology and so themselves have to be defined.These are the hand-crafted, single axial taxonomiesdescribed in the normalization stage in the sectionon Normalization and Modularization of ontoge-nies, above. The nature of these elemental defini-tions can range from a simple taxonomy to complexdissections in their own right. Knowledge is fractal,and the decision about ‘how far down to model’ isbased on the degree of knowledge the final ontol-ogy needs to support. For example, GO is not usedto annotate information concerning detailed chem-ical atomic substructure, and so modelling that

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

Concept name heparin biosynthesis

DAML + OILdefinition

class heparin biosynthesis definedsubClassOf biosynthesisrestriction onProperty acts−on hasClass heparin

(acts−on is a unique property)

Paraphrase biosynthesis which acts solely on heparin

Concept name glycosaminoglycan biosynthesis

DAML + OILdefinition

class glycosaminoglycan biosynthesis definedsubClassOf biosynthesis

restriction onProperty acts−on hasClassglycosaminoglycan

Paraphrase biosynthesis which acts solely on glycosaminoglycans



is-a cell growth and/or maintenance

metabolism

heparin biosynthesis

carbohydrate metabolism

aminoglycan metabolism

biological processis-a

is-a

is-a

is-a

is-a

is-a

is-a

glycosaminoglycan metabolism

heparin metabolism

heparin biosynthesisis-a

is-a glycosaminoglycan biosynthesis

Figure 4. Directed acyclic graph showing additional parent for heparin biosynthesis found using the reasoner

knowledge would be fruitless at this stage. Once allthe explicit terminological knowledge is in place,we can submit it to the reasoner and examine thereport of the new subsumption links it has inferred,what links are redundant, and what concept defini-tions are logically inconsistent.

Reassuringly, the changes reported by the DLreasoner represent mostly additional relationshipshard to spot by human eye, and not errors inbiological knowledge.

For example, the reasoner reported that heparinbiosynthesis has a new ‘is-a’ parent, ‘glycosamino-glycan biosynthesis’. Figure 4 shows the hierar-chy that results from these descriptions being sub-mitted to the reasoner; it can be compared withFigure 3. These reports can then be sent to theeditorial team for comment and action if neces-sary. Although the nature of the changes made bythe reasoner are limited and may not be acceptedby the GO team as the correct solution, in termsof the biology, they are helpful in pointing outareas of the ontology which may need attention.For example, inferring a new subsumption rela-tionship to a very general concept may point outthe need for new ontology fragments incorpo-rating new intermediate concepts. The reasonerreported that ‘taurine catabolism’ GO0019529 hasnew super ‘catabolism’ GO0009056. This led tothe GO editor to actually make ‘taurine catabolism’

a child of the more specific term, ‘amino acidderivative catabolism; GO0042219’ instead. Theeditor then created a new term, ‘taurine biosyn-thesis; GO0042412’ and made it a child of both‘taurine metabolism; GO0019530’ and ‘amino acidderivative biosynthesis; GO0042398’.

Although the GONG project has only tackleda small portion of GO, it has shown the utilityof such an approach. In our first experiment, wemigrated 350 metabolism terms to the DAML +OIL, property-based form. From these descriptions,22 missing ‘is-a’ relationships were found. Thismeans that nearly one in ten concepts in thehand-crafted form of GO were not completelydescribed. This does not mean that GO is bad orwrong — it is another demonstration that buildingontologies is difficult. As already mentioned, fewreal biological errors have been found — mostwere errors of omission. We feel that we havealready begun to show the utility of an approachin which it is possible to use one knowledgerepresentation language to both represent a hand-crafted ontology and migrate it in situ to a moreexpressive, property-based form.

Discussion

In this article we have presented an approach torepresenting ontologies used in the Bioinformatics



and Medical Informatics groups at the Universityof Mancherster. We are now using DAML + OILas our knowledge representation language. Thisallows us to use ontologies represented in a varietyof forms, from hand-crafted taxonomies of phrasesto full-blown concept definitions based upon theuse of a concept’s properties. In the latter form,DL-based reasoners can be used to infer the classi-fication encoded within the descriptions of a con-cept’s properties.

Such a DAML + OIL-based representationallows ontologies to be as simple as possible,yet as complex as necessary. In addition, we canmigrate from the simpler form to the more com-plex, reasoning-supported form without having tothrow away the simpler representation. Due to thisrange of entry levels, it is possible to includeexperts in the domain, rather than in DL’s, in theprocess of constructing DAML + OIL ontologies,allowing exploitation of their specialist knowledge.

Although with tools such as OilEd and the rea-soners it encompasses, we have the foundationsof a full ontology development environment andmethodology, much work remains to be done. Aswell as the extensions needed for OilEd describedabove, some barriers still remain to non-specialistuse of DAML + OIL. Using the full expressivityof DAML + OIL is still difficult, particularly theuse of elements such as universal and existentialquantification. GONG has begun to develop meth-ods and tools to support the migration of termsto property descriptions through automated dissec-tions. However, techniques that will allow high-expressivity not to be a barrier between expert andrepresentation still need to be developed and arean active area of research. Nevertheless, we feelthat the approach advocated at Manchester formsthe basis for moving arguments from ‘What is anontology?’ to ‘How do we best deliver ontologiesthat serve the purposes we require?’

Acknowledgements

Chris Wroe is supported by the myGrid e-Science pilot(EPSRC GR/R67743) and GONG (DARPA DAML sub-contract PY-1149 from Stanford University). Phil Lord isalso supported by the myGrid project. Sean Bechhofer is

supported by EU Grant IST-2001-33052. We would like tothank Midori Harris and Jane Lomax of the GO editorialteam for their advice and for responding to the reports.

References

1. Rogers J, Roberts A, Solomon D, et al. 2001. GALEN tenyears on: tasks and supporting tools. In MedInfo 2001 Studiesin Health Technology and Informatics, Haux R, Rogers R,Patel V (eds). IOS Press: Amsterdam; 256–260.

2. Goble CA, Stevens R, Ng G, et al. 2001. Transparent Accessto Multiple Bioinformatics Information Sources. IBM Systems,(special issue on deep computing for the life sciences) 40(2):532–552.

3. Wroe C, Stevens RD, Goble CA, Roberts A, Greenwood M.2003. A suite of DAML + OIL Ontologies to describebioinformatics services and data. Int J Coop Inf Syst (acceptedfor publication).

4. Wroe CJ, Stevens RD, Goble CA, Ashburner M. A method-ology to migrate the Gene Ontology to a description logicenvironment using DAML + OIL. 8th Pacific Symposium onBiocomputing (PSB) (accepted for presentation, 2003).

5. The Gene Ontology Consortium. 2000. Gene Ontology: toolfor the unification of biology. Nature Genet 25: 25–29.

6. World Health Organization. 1992. International StatisticalClassification of Diseases and Related Health Problems, 10threvision (ICD-10). World Health Organization: Geneva.

7. Rogers JE, Price C, Rector AL, Solomon WD, Smejko N.1998. Validating clinical terminology structures: integrationand cross-validation of Read Thesaurus and GALEN. In AMIAFall Symposium, Lake Buena Vista, FL, USA.

8. Swiss-PROT Keywords: http://www.expasy.ch/cgi-bin/keywlist.pL

9. Baader F, McGuinness D, Nardi D, Schneider PP (eds). 2003.The Description Logic Handbook: Theory, Implementation andApplications. Cambridge University Press: Cambridge.

10. Horrocks I. 2002. DAML + OIL: a reason-able web ontologylanguage. In Proc. EDBT 2002. Lecture Notes in ComputerScience. Springer-Verlag, Heidelberg, 2–13.

11. Stevens R, Horrocks I, Goble C, Bechhofer S. 2001. Buildinga reasonable bioinformatics ontology using OIL. InProceedings of the IJCAI 2001 Workshop on Ontologies andInformation Sharing, 81–90.

12. OilEd: http://oiled.man.ac.uk13. GONG: http://gong.man.ac.uk14. myGrid: http://mygrid.man.ac.uk15. DAML + OIL semantics: http://www.damL.org/2001/03/

model-theoretic-semantics.htmL16. FaCT: http://www.cs.man.ac.uk/fact17. Franconi E, Ng G. 2000. The i.com tool for intelligent

conceptual modelling. In 7th Intl. Workshop on KnowledgeRepresentation meets Databases (KRDB’00).


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