The Montagues and the Capulets

Comparative and Functional Genomics Comp Funct Genom 2004; 5: 623–632. Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/cfg.442 Conference Paper The Montagues and the Capulets

Carole Goble* and Chris Wroe The School of Computer Science, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK

*Correspondence to: Abstract Carole Goble, The School of Computer Science, The University of Manchester, Oxford Road, Prologue Manchester, M13 9PL, UK. Two households, both alike in dignity, In fair Genomics, where we lay our scene, (One, E-mail: carole.goble/c.wroe@ comforted by its logic’s rigour, Claims ontology for the realm of pure, The other, manchester.ac.uk with blessed scientist’s vigour, Acts hastily on models that endure), From ancient grudge break to new mutiny, When ‘being’ drives a ﬂy-man to blaspheme. From forth the fatal loins of these two foes, Researchers to unlock the book of life; Whole misadventured piteous overthrows, Can with their work bury their clans’ strife. The fruitful passage of their GO-mark’d love, And the continuance of their studies sage, Received: 11 November 2004 Which, united, yield ontologies undreamed-of, Is now the hour’s trafﬁc of our stage; Revised: 15 November 2004 The which if you with patient ears attend, What here shall miss, our toil shall strive Accepted: 16 November 2004 to mend. Copyright  2005 John Wiley & Sons, Ltd.

Stage notes (Joslyn et al., 2004) and over 60 were locked out of the room demanding entry. 1. This paper is a write-up of the opening plenary The effective development of large ontologies, talk of the SOFG2 conference (http://www.sofg. and their wide deployment, requires appropriate org/meetings). Delegates throughout the rest of languages and mechanisms. We need languages the meeting named themselves as Montagues that permit the formal and explicit specification and Capulets — which was revealing in itself. of the meaning of terms, so that these mean- 2. For the sake of effect, we make sweeping gener- ings are machine-interpretable, can be unambigu- alizations. ously shared and can be used to computation- ally infer new knowledge. We also need mechanisms for ontology development, deployment and We lay our scene maintenance. Conveniently, the Computer Sci- ence/Artificial Intelligence communities work on In recent years, ontologies have taken centre stage knowledge representation techniques and technolo- as their importance within life sciences grows. gies that should benefit the Life Scientist. Life Interoperating resources, intelligent mining and Scientists, in turn, supply the Computer Scientists sharing knowledge, be it by people or computer with practical, realistic problems as an ideal source systems, requires a consistent shared understand- of requirements, and provide a community of early ing of what the information means. The life sci- adopters to pilot their solutions. However, despite ence community have an immediate and press- the obvious mutual benefit, the two communities ing need for controlled vocabularies if they are to often find themselves in conflict, mostly due to mis- successfully glue together and classify the numer- understandings of the motivations that lie behind ous results populating their expanding collection of the communities, a lack of awareness of the aspects data resources. As a measure of the interest in the of their own characters that frustrate the other and, topic, over 700 people attended the opening paper perhaps, a failure to recognize that collaboration of the ontology track at ISMB 2004 in Glasgow will mean compromise. It was ever thus. We have

Copyright  2005 John Wiley & Sons, Ltd. 624 C. Goble and C. Wroe a roadmap to chart the rivalry and reconciliations efforts from this community. For example, Open- between these two Houses (Shakespeare, 1596). We cyc currently has around 60 000 terms describing follow this to make explicit the characters of these ‘common ideas’ made publicly available from the two Houses (or three as it turns out), highlight some 260 000 or more concepts of the Cyc ontology of the reasons for their quarrels, and identify oppor- (http://www.opencyc.org). Despite the fact that tunities for reconciliation that we hope will lead to much of the work is with application stakeholders, a happy outcome, rather than a tragedy. Montagues tend towards ‘technology push’, using the application as an experimental sand box during their pursuance of academic excellence. This The Houses of Genomics House has been active for around four decades and during that time have developed a startling Bioinformatics is already an interdisciplinary topic and confusing number of languages — CycL, KL- encompassing the many disciplines of the ‘omi- ONE, RDF, RDFS, OIL, DAML + OIL, OWL, cs’ — genomics, proteomics, metabolomics, tran- RuleML, SWRL (Gomez-P´ erez´ and Corcho, 2002; scriptomics — together with chemoinformatics, Horrocks et al., 2004) — and the tools to go with medical informatics, phenotypical observation, them — FaCT, RACER, OilEd, Proteg´ e,´ Proteg´ e-´ phylogeny, anatomy and so on. This mixing of dis- OWL, OntoBroker, Jena (Denny, 2004). Their e- ciplines is itself a challenge and, added to that, commerce cousins have added to the mix with is the challenge of underpinning the bioinformat- Topic Maps, UML, RosettaNet and ebXML. This ics by introducing Computer Scientists. In addi- activity has escalated in recent years, motivated by tion, the fields of ontology and knowledge man- the Semantic Web vision, which has led this com- agement have their own communities. Thus, in fair munity to engage intensely with the standardization Genomics, where we lay our scene there are a num- activities of the W3C. ber of Houses. In fact there are three, rather than the The Semantic Web is an extension of the current traditional two — Computer Scientists, Life Scien- Web, in which information is given well-defined tists and Philosophers. meaning, better enabling computers and people to work in cooperation. In practice, this is exposing The Montagues the meaning of Web resources by assertions in a common data model, Resource Description Frame- One, comforted by its logic’s rigour/Claims ontol- work (RDF), and the publication and sharing of ogy for the realm of pure. This is the House of consensually agreed ontologies in RDF Schema Computer Science, knowledge management and (RDFS) or Web Ontology Language (OWL), so artificial intelligence (AI). Their interests lay in that metadata can be shared and background knowl- the logics and languages needed for the organiza- edge can be declared. We use this semantic fabric tion and representation of ontologies and knowl- to query, filter, integrate and aggregate the meta- edge bases that can support intelligent reason- data, and reason over the metadata and ontologies ing and logical inference. Theory is their strong to infer more metadata. To declare a measure of point, with a traditional desire for orderliness, con- confidence in the assertions and inferences, we sistency, coherency and proof. They like their attribute trust to the metadata and proof to the knowledge to be well behaved and have devel- inferences. The idea is to create a platform for oped methodologies to build ontologies cleanly automated, computational, sentient agents to oper- from the top down, from scratch, with good princi- ate over. Then these agents can dynamically dis- ples. Because they are developing techniques for cover and combine resources and applications on all applications, their results are expected to be behalf of users, e.g. to book a medical appointment generic. They have example ontologies but, as or make travel arrangements (Berners-Lee et al., this community typically is concerned with the 2001). As a consequence, the Montagues are cur- mechanics of the ontology rather than its content, rently found in the World Wide Web Consortium the examples are usually small and pathologically (W3C) Semantic Web and Semantic Web Services designed to test the boundaries of the expressive- Activities. They have also had to become more ness of languages or challenge reasoning engines. tolerant of confusion as, unlike their traditional However, there are some examples of content knowledge bases, the Web is messy — inconsistent

Copyright  2005 John Wiley & Sons, Ltd. Comp Funct Genom 2004; 5: 623–632. The Montagues and the Capulets 625 metadata, multiple and overlapping ontologies, and available is appropriate. The Capulets inhabit an competing and conflicting logical claims that make increasingly crowded landscape as life sciences reasoning tricky. move to systems biology. The medical informatics and healthcare community has many ontologies The Capulets of its own and increasingly this world needs to be linked to that of genomics. The other, with blessed scientist’s vigour/Acts hastily on models that endure. This is the House of The Philosophers the Life Scientists. The world of bioinformatics is of pragmatics and practice, with a strong applica- One, comforted by its logic’s rigour/Claims ontol- tion pull. Their motivating vision is one of well- ogy for the realm of pure. The Montagues have structured controlled vocabularies for information cousins. These are also firmly in the realm of the- sharing, classification and indexing. These are used ory, but whereas the Montagues concentrate on the to enhance accurate retrieval, create common stan- representation of conceptual models of truth that dards for annotation and support the mediation aid an application, the Philosophers seek a single between and interlinking of the contents of dif- model of truth itself. Some even believe in one ferent databases. Capulets have been classifying universal, unifying ontology. They build founda- animals since Aristotle and Linnaeus. Increasingly, tional ontologies such as SUO (the Standard Upper ontologies are being used for applications other Ontology) or DOLCE (a Descriptive Ontology for than annotation, such as data mining. Whereas Linguistic and Cognitive Engineering) (Lehmann, the Montagues see knowledge representation as 2004), that contain concepts such as ‘perdurant’ an end in itself, the Capulets see it as a means (an entity that extends in time but is not wholly to an end, and that end is Science. Their oper- present at any one time) and ‘endurant’ (an entity ating timescale is immediate; they have a prob- that is wholly present through time). Their moti- lem now and they are in a hurry. Thus, their vations are the theories of parts and wholes (i.e. approach is ‘build it, use it, and fix it later’. mereology), essence and identity, dependence qual- Ontologies have typically been seeded from key- ities, composition and constitution, participation word lists or by small groups of highly moti- and representation, which they claim should form vated service providers/users. They have been put the foundation of all ontologies. However, their to use immediately, so there is no futile attempt contributions are helpful — they have developed to ‘get it right first’. Consequently, methods for methodologies and patterns that are relevant to Life evolution and change have been present from the Scientists, e.g. when modelling parts and wholes of start. Drawing from their legacy of database cura- biological structures. They are not usually, how- tion practice, the Capulets have developed work- ever, concerned with earthly pursuits such as tools, able methodologies for consensually developing and take the high intellectual ground when it comes community-wide ontologies, supported by sophisti- to academic scholarship. They are also notoriously cated infrastructure (Bada et al., 2004). The ontolo- argumentative, not given to building consensus, gies are specific rather than generic, focusing on and have been arguing since Aristotle’s time. Their gene products, microarray experiments, sequences, operating timescale is ‘as long as it takes’. anatomy, etc. At the time of writing, 39 were avail- We have three Houses, as shown in Figure 1. able from the Open Biological Ontologies website Each speaks a different language, each has a (http://obo.sourceforge.net), a gathering place of different agenda, each plays a different role and the community. Most are simple in their struc- each works to a different timescale. In some ways ture — graphs or taxonomies — but their cover- these differences are beneficially complementary, age, relevance and take-up is significant and real. but they also sow the seeds of conflict. The favourite child of the Capulets is the Gene Ontology, an international effort of over 18 000 concepts with wide adoption that has made a sig- Duels nificant impact (GO Consortium, 2000). They are keen on using standards and tools, but do not From ancient grudge break to new mutiny/When hesitate to construct their own if none of those ‘being’ drives a fly-man to blaspheme. ISMB 1998

Figure 1. The three Houses of Genomics, showing their different characters, different languages and how they might be united through the Life Science Semantic Web hosted the first Bio-Ontologies workshop, which cost–benefit proposition for a particular applica- was an energetic affair. On the positive side, tion may not warrant such an effort; Wroe et al. representatives of the Houses came together; on the (2003) outline the work needed to migrate from negative side, the divisions between the hardcore the current Gene Ontology to DAML + OIL. members became apparent. Notably, a presentation Using a fraction of the expressivity of a language of a foundational bio-ontology commencing with and adopting a common exchange language has the concept ‘being’ led a breakaway group of major benefits in itself (Stevens et al., 2003). Capulets, led by Professor Michael Ashburner, to • Nine items or less. Present an invented ontology found the Gene Ontology. Since then there have example made up of 20 complicated artificial been other duels, usually started by the same old concepts, using every technical feature available, quarrels. As a service to genomics, here is a guide about wine or Clyde the elephant (a common toy to the best ways to start an argument. example used in AI publications). • Suits me. Offer an ontology tool that is straight- How to frustrate a Capulet if you are a forward if you have a background in logic Montague but exposes the underlying formalisms in an unintelligible way to a domain scientist. This • All or nothing. Argue or imply that unless says, ‘if you become like me, you too can use you are using all of the expressivity and rea- this’ — which is at best unhelpful. soning capabilities of a knowledge representa- • Keep still. Present mechanisms that support cre- tion language, e.g. OWL-DL (Horrocks et al., ation of an ontology from scratch, but do not 2003), then you shouldn’t use it at all. A Mon- support changes or versioning of either the ontol- tague often sees the language features but not ogy itself, or the metadata that uses it. In fact, the the amount of effort needed to use them. The response ‘that’s a research topic’ to a predictable

problem associated with an ontology as a con- fun), than take a simpler ‘good-enough’ route. sequence of it being living and working will be At the heart of this lies the different agendas of greeted with a sigh. Ontologies are conceptual- the two Houses: Capulets want to link together izations of consensual knowledge of a commu- scientific data well enough to get on with sci- nity — the consensus changes, the communities ence, whereas Montagues want to build sentient evolve, and the conceptualizations change. applications. • We know best. ‘Tell us what you want to say and we will build it for you’. Twenty years of work on knowledge elicitation and knowl- How to frustrate a Montague if you are a edge acquisition lead to a view that a ‘sub- Capulet ject matter expert’ tells their knowledge to a knowledge engineer, who then encodes it. In There are two sides to every story: the complex world of molecular biology research and related disciplines, where knowledge is the • Repeat the same old mistakes. Make the same point of the scientific endeavour, this is at mistakes and the same misunderstandings, over best a conceit. In the Halo project experiment and over again. Montagues have a wealth of (http://www.projecthalo.com/), analysis of the experience in modelling, e.g. in mereology (Win- mistakes in the ontologies built showed them to ston et al., 1987) and the differences between be misunderstandings and simplifications by the instances and concepts (Noy and McGuinness, knowledge engineers. 2001). They know that simple approaches using • The finished product. Refuse to release the ontol- directed acyclic graphs do not gracefully scale. ogy until it is ‘finished’, and when it is finished Manually predetermining and classifying every be astonished that the users won’t use it. This combination of every term is unnecessary and is often because the ontology is designed in unsafe when logic languages automatically offer terms of the knowledge engineer, not the domain assistance (Rogers et al., 1998). Simplifications expert. Until surprisingly recently, the knowl- made early on in the development of an ontol- edge management community concentrated on ogy, for understandable reasons, store up trou- the early parts of the knowledge life cycle, ble down the road that can be foreseen if one neglecting maintenance, in particular contin- is willing to pay the cost now rather than ued distributed development by a large number later. For example, combinatorial explosion of of knowledge contributors. Even today, many metabolic processes in the Gene Ontology even- knowledge acquisition tools remain unconnected tually becomes difficult to maintain by hand and to ontology editors. Honourable exceptions exist, will lead to incompleteness in the structure and such as the GALEN tools for clinical ter- reduced performance in its intended database minology development — we observe that this retrieval task (Wroe et al., 2003). was an application-driven project (Rogers et al., • It works. Hack together a mechanism, tool or 1997). Proteg´ e-OWL,´ part of the Co-ode project application and declare it to work (with no evi- (http://www.co-ode.org), aims to eliminate the dence what that means) for a specific example, knowledge engineer middleman from the knowl- with no guarantee that it will work with any edge acquisition process and support domain other data. Montagues are driven by generic experts to accurately and effectively build their solutions that are explainable, repeatable, sus- own ontologies, drawing on the GALEN experi- tainable and independent of freaks of data. They ences. abhor baroque solutions with a large ‘exceptions’ • Not my problem. Offer to solve a different prob- case load. lem than the one actually presented. Maybe the • Ignorance is bliss. Ignore the past four decades problem can be dealt with by a sociological solu- of reasoning and knowledge representation rese- tion, which hardly ever attracts a Montague’s arch, along with the understanding of the bound- interest but is feasible by well-organized com- aries of expressivity of languages and the algo- munities of curators and knowledge contributors. rithms that infer knowledge using them. A lan- Often a Montague would rather solve a harder guage with every construct in it, like OWL-Full, problem (that to them is more interesting and is not decidable. Error tolerance is not the same

as ignorance of errors and inconsistencies. Just as the Houses. The issue comes down to practical our understanding of the genome has advanced engagement with those actually building the ontol- astonishingly over the past decade, so astonish- ogy. ing advances have been made in computing in • understanding of the decidability and tractability Finger pointing from the sidelines. Declare that of expressive knowledge languages. the hard working ontology curators are not doing • I know what it means — but that doesn’t mean it right but do not tell them why, or give them that anyone or anything else will. Machine- any practical tools or guidance. Do not tell them computable ontologies need formal and explicit directly, using their public curation policies and semantics. A shared common understanding mechanisms, but instead make public statements or whisper in back rooms. requires unambiguously specified clear seman- • tics. I wouldn’t start from here. Declare that the • I tried your software and it broke/stopped res- ontology should be started again, ignoring the ponding, so I went off and built my own. The effort needed and its extensive legacy, and offer no migration path for this legacy. majority of life science ontologies are large; the • Gene Ontology currently stands at 18 000 con- Mismatched expectations. Complain that the cepts. These are much larger than the examples ontology is not a model of true knowledge or used to develop the Montagues’ software, which does not fit a different purpose to the one for in the past has struggled to cope. Scalability and which it was developed. An ontology developed performance do matter but are not usually the to annotate database entries is not intended to be top priority for the Montagues. a complete model of ‘truth’ and may well not • suit an alternative application. Keep it simple. A little semantics goes a long • way; what a waste of the language when its rich- Truth and beauty. Declare that ‘truth’ is more ness is ignored! There is plenty of complexity to important than practicality. In reality, consensus be had if it can be managed, but a Capulet will is more important than truth and perfection is not make something complex if simple works the enemy of the good. What is truth? German well enough. taxonomists considered the giant panda to be a • Consensus outweighs complexity. The realiza- bear; British taxonomists that it was a racoon; tion that no matter how simply structured or and American taxonomists didn’t know which it scruffy the ontology, what counts is if every- was. Now it is defined as a bear — because the one uses it, e.g. the SOFG Anatomy Entry List community has agreed on the definition of a bear and state it is a bear, not because it is the ‘truth’. (http://www.sofg.org/sael/) is a just a list of few • hundred terms but enough to bind a community. There are no such things as concepts. Take an Similarly, it doesn’t matter how smart and sound esoteric stand that might win accolades in a and complete the ontology is, if no one uses it, it journal of philosophy but is unhelpful in practice. doesn’t count. To date, it is hard to find a ‘smart’ ontology that has made it into widespread use. Balcony scenes SNOMED-RT, and its successor SNOMED-CT, is smart on the inside for maintenance purposes From forth the fatal loins of these two foes/Resear- (Spackman et al., 1997), but goes through a pro- chers to unlock the book of life;/Whole misadven- cess of semantic materialization — which turns tured piteous overthrows/Can with their work bury it into graphs — to make it appear simple to their clans’ strife. Amid these arguments there are healthcare applications and users. wonderful examples of reconciliation and mutual support. Many Capulets and Montagues are working closely together, and each complements the How to frustrate a Montague or a other, as we show in Figure 2. Capulet if you are a Philosopher

The Philosophers have centuries of modelling Using the W3C RDF/OWL standards experience and thought but can comfortably start Both houses are in favour of standards — using an interminable argument with all and any of them and creating them. The Montagues have

(a)

(b)

Figure 2. The current state of the art in bio-ontologies. (a) What the Montagues bring to the party. (b) The Capulets’ contribution done a great job of producing a standard ontology the RDF language for describing assertions using language, OWL (Horrocks et al., 2003), for the ontology terms. The Capulets have proved to be Semantic Web that draws from their years of enthusiastic early adopters of these languages. The research and practice. They have also produced Open Biological Ontologies (http://obo.sourcefor-

Copyright  2005 John Wiley & Sons, Ltd. Comp Funct Genom 2004; 5: 623–632. 630 C. Goble and C. Wroe ge.net/) consortium mandates OWL as one of [e.g. in the Sequence Ontology (http://song.sour- its preferred exchange languages; new ontologies, ceforge.net/) and the Foundational Model of An- such as BioPAX (http://www.biopax.org/), are atomy (http://sig.biostr.washington.edu/projects/ using OWL from the outset. Other work on the fm/)] and expanding to relationships other than is-a Gene Ontology uses language processing and pat- and part-of [e.g. in the Chemical Entities of Bio- terns to extract implicit knowledge within it and logical Interest dictionary (http://www.ebi.ac.uk/ uses reasoning to identify additional subsump- chebi/)]. Relationships in biology do not have the tion relationships and inconsistencies (Wroe et al., baggage that concepts have, and so represent a 2003). fresh opportunity for bio-ontologists.

The Age of Reasoning Methods Hand in hand with the adoption of OWL is the The Gene Ontology effort has created a gold movement towards the judicial use of reasoning standard method for community-wide consensual necessary to support the scale of the ontologies development of a working and living ontology needed by the community. This includes figuring (Bada et al., 2004). Given that this works in prac- out when reasoning provides the best benefits tice, it is reassuring that more or less the same in the ontology life cycle. Modelling using a approach has now been proposed by the Montagues compositional, term coordination approach (instead in the DILIGENT methodology (Pinto et al., 2004), of pre-enumerating and classifying every term by and thereby works in theory too. Incremental evolu- hand) and a stronger emphasis on relationships tion is a day-to-day occurrence dealt with by highly between concepts motivates a need for reasoning, curated ontologies like GO, and by such method- but reasoning is not the be-all and end-all. ologies. Paradigm revolutions — where current scientific orthodoxy is overthrown, requiring a new Tools ontology that is not an incremental extension of the old but is incompatible with the old — are less well We see a convergence of ontology creation tools supported, and a present a major technical and soci- as the ontologies become more sophisticated, yet ological challenge. If the effort to support paradigm this complexity needs to be simplified for the user. shifts is too large we are in danger of fossilizing OBO-Edit (https://sourceforge.net/project/show- our knowledge. Experiences from the life sciences files.php?group id=36 855) is becoming more like have shown that the success of large-scale ontol- the full Proteg´ e-OWL´ editor (Knublauch et al., ogy building is more sensitive to social or political 2004), which in turn is adopting wizards and processes than technology. plug-ins to simplify and specialize interaction with the ontologies, like OBO-Edit. Tools like XSPAN Marriage or poison? (http://www.xspan.org/), developed for the life science community, can contribute more generally. So our scene is laid. Can we help each other The challenge is to lower the barriers of entry for to shelter from the maelstrom of standards, lan- developers and knowledge contributors, and pro- guages, prototypes, tools, content and commercial vide ‘invisible’ tooling for end applications. We offerings? Is a marriage between our Houses pos- need tooling for vocabulary management and appli- sible? One such bond is the Semantic Web ini- cation developers, rather than core ontology devel- tiative. It has motivated the Montagues to pro- opment. We need scalable, efficient reasoning and duce standard languages for ontologies and to RDF stores capable of dealing with millions of tackle issues they had previously ignored, such assertions. as incomplete and inconsistent knowledge. The Capulets have already reaped the benefits. The Ontology patterns Web needed incubation in a friendly, contained and forgiving community with a true distributed Presumably to the Philosophers’ delight, the Capu- information problem (physics); the Semantic Web lets are turning to mereology for the more princi- would benefit from the same and the ideal bed- pled and systematic representation of taxonomies fellow is life sciences. In October 2004 W3C

Copyright  2005 John Wiley & Sons, Ltd. Comp Funct Genom 2004; 5: 623–632. The Montagues and the Capulets 631 hosted the first Semantic Web for Life Sci- who bridge all communities, typically originally ences meeting (http://www.w3.org/2004/07/swls- trained in medical or life sciences, such as Alan ws.html), which attracted over 100 delegates. Rector and Mark Musen): Semantic Web technologies such as RDF and OWL, with domain-specific standards like LSID Rebellious subjects, enemies to peace Throw down your mistemper’d weapons to the ground (Clark et al., 2003), represent an evolvable, inter- Go hence, to have more talk of these top ‘things’; operable and fundamentally network-driven appro- All should be understood, respect’d, and well-found: ach to information and knowledge aggregation. As For never was a story with more GO such, they appear to represent an opportunity for Than this of AI, life science and the O. developing solutions to overcome some of the difficult technology issues in life sciences — disparate, Acknowledgements constantly evolving data sources and ontologies (in both public and private settings) and the need to We would like to thank Robert Stevens, Chris Catton aggregate the data and ontologies into a resource and William Shakespeare for ideas that contributed to this article and the SOFG talk that originated it. that can be queried, securely, and result in an audit trail. However, this marriage could become poison for References the Capulets if it is not entered into as an equal partnership. For example, during standardization Bada M, Stevens R, Goble CA, et al. 2004. A short study on the process of OWL-DL, the expressivity to describe success of the Gene Ontology. J Web Semant 1: 235–240. qualified number restrictions — the ability to say Berners-Lee T, Hendler J, Lassila O. 2001. The semantic web. that a normal hand has five fingers and one Scientific American May. of these must be a thumb — was abandoned. BioPAX; http://www.biopax.org/ Chemical Entities of Biological Interest dictionary; http://www. The technical know-how is well understood; it is ebi.ac.uk/chebi/. logically expressible and tractable for reasoning. It Clark T, Martin S, Liefeld T. 2004. Globally distributed object isn’t there because the W3C standards committee identification for biological knowledgebases. Brief Bioinform 5: did not fully realize its obvious crucial importance 59–70. to the life science community until it was too CO-ODE; http://www.co-ode.org Denny M. 2004. Ontology tools survey, revisited. O’Reilly late, and the community did not voice its concerns xml.com; http://www.xml.com/pub/a/2004/07/14/onto.html clearly and loudly. The W3C Semantic Web Best (accessed 8 November 2004). Practice Activity has taken care not to make the The Gene Ontology Consortium. 2000. Gene Ontology: tool for same mistake. The Capulets must ensure that their the unification of biology. Nature Genet 25: 25–29. interests are being served and that they are not Foundational Model of Anatomy; http://sig.biostr.washington. edu/projects/fm/ merely an expedient test case for the Montagues. Gomez-P´ erez´ A, Corcho O.´ 2002. Ontology specification lan- That means they must engage with them and guages for the semantic web. IEEE Intell Syst 17: 54–60. their language design efforts, their standardization Halo; http://www.projecthalo.com/ activities and their tool building. Horrocks I, Patel-Schneider PF, van Harmelen F. 2003. From Romeo and Juliet is a tragedy. How do we turn SHIQ and RDF to OWL: the making of a web ontology language. J Web Semant 1: 7–26. our story into a happy ending? Here is a desiderata Joslyn CA, Mniszewski SM, Fulmer A, Heaton G. 2004. The for a good marriage. Collaboration through Con- Gene Ontology categorizer. Bioinformatics 4(suppl 1): versation; set aside the time and patience to over- I169–I177. come the language barriers. Hold mutual Respect Knublauch H, Fergerson R, Noy NF, et al. 2004. The Proteg´ e-´ and Understanding for the other’s motivations and OWL plugin: an open development environment for semantic web applications. Third International Semantic Web Conference. contributions. Avoid being judgemental. Compro- ISWC2004 Lecture Notes in Computer Science, 3298. Springer- mise should be viewed as a success and not a Verlag: Heidelberg 229–243. failure. Work as a Partnership. Take a look at Lehmann J, Borgo S, Masolo C, Gangemi A. 2004. Causality each duel point and think about it. Do you say and causation in DOLCE. Proceedings of the International that? Do you hear that? How can we benefit from Conference on Formal Ontology in Information Systems, FOIS 2004. 114: Frontiers in Artificial Intelligence and Applications. each other’s experience and results? MGED; http://www.mged.org/ As the Prince of Genomics might say (echoing Noy NF, McGuinness DL. 2001. Ontology Development 101: the Prince of Verona; our peacekeepers are those A guide to creating your first ontology. Stanford Medical

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