the montagues and the capulets

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

*Correspondence to:Carole Goble, The School ofComputer Science, The Universityof Manchester, Oxford Road,Manchester, M13 9PL, UK.E-mail: carole.goble/[email protected]

Received: 11 November 2004Revised: 15 November 2004Accepted: 16 November 2004

Abstract

PrologueTwo households, both alike in dignity, In fair Genomics, where we lay our scene, (One,comforted by its logic’s rigour, Claims ontology for the realm of pure, The other,with blessed scientist’s vigour, Acts hastily on models that endure), From ancientgrudge break to new mutiny, When ‘being’ drives a fly-man to blaspheme. Fromforth the fatal loins of these two foes, Researchers to unlock the book of life; Wholemisadventured piteous overthrows, Can with their work bury their clans’ strife. Thefruitful passage of their GO-mark’d love, And the continuance of their studies sage,Which, united, yield ontologies undreamed-of, Is now the hour’s traffic of our stage;The which if you with patient ears attend, What here shall miss, our toil shall striveto mend. Copyright 2005 John Wiley & Sons, Ltd.

Stage notes

1. This paper is a write-up of the opening plenarytalk of the SOFG2 conference (http://www.sofg.org/meetings). Delegates throughout the rest ofthe meeting named themselves as Montaguesand Capulets — which was revealing in itself.

2. For the sake of effect, we make sweeping gener-alizations.

We lay our scene

In recent years, ontologies have taken centre stageas their importance within life sciences grows.Interoperating resources, intelligent mining andsharing knowledge, be it by people or computersystems, requires a consistent shared understand-ing of what the information means. The life sci-ence community have an immediate and press-ing need for controlled vocabularies if they are tosuccessfully glue together and classify the numer-ous results populating their expanding collection ofdata resources. As a measure of the interest in thetopic, over 700 people attended the opening paperof the ontology track at ISMB 2004 in Glasgow

(Joslyn et al., 2004) and over 60 were locked outof the room demanding entry.

The effective development of large ontologies,and their wide deployment, requires appropriatelanguages and mechanisms. We need languagesthat permit the formal and explicit specificationof the meaning of terms, so that these mean-ings are machine-interpretable, can be unambigu-ously shared and can be used to computation-ally infer new knowledge. We also need mecha-nisms for ontology development, deployment andmaintenance. Conveniently, the Computer Sci-ence/Artificial Intelligence communities work onknowledge representation techniques and technolo-gies that should benefit the Life Scientist. LifeScientists, in turn, supply the Computer Scientistswith practical, realistic problems as an ideal sourceof requirements, and provide a community of earlyadopters to pilot their solutions. However, despitethe obvious mutual benefit, the two communitiesoften find themselves in conflict, mostly due to mis-understandings of the motivations that lie behindthe communities, a lack of awareness of the aspectsof their own characters that frustrate the other and,perhaps, a failure to recognize that collaborationwill 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 reconciliationsbetween these two Houses (Shakespeare, 1596). Wefollow this to make explicit the characters of thesetwo Houses (or three as it turns out), highlight someof the reasons for their quarrels, and identify oppor-tunities for reconciliation that we hope will lead toa happy outcome, rather than a tragedy.

The Houses of Genomics

Bioinformatics is already an interdisciplinary topicencompassing the many disciplines of the ‘omi-cs’ — genomics, proteomics, metabolomics, tran-scriptomics — together with chemoinformatics,medical informatics, phenotypical observation,phylogeny, anatomy and so on. This mixing of dis-ciplines is itself a challenge and, added to that,is the challenge of underpinning the bioinformat-ics by introducing Computer Scientists. In addi-tion, the fields of ontology and knowledge man-agement have their own communities. Thus, in fairGenomics, where we lay our scene there are a num-ber of Houses. In fact there are three, rather than thetraditional two — Computer Scientists, Life Scien-tists and Philosophers.

The Montagues

One, comforted by its logic’s rigour/Claims ontol-ogy for the realm of pure. This is the House ofComputer Science, knowledge management andartificial intelligence (AI). Their interests lay inthe logics and languages needed for the organiza-tion and representation of ontologies and knowl-edge bases that can support intelligent reason-ing and logical inference. Theory is their strongpoint, with a traditional desire for orderliness, con-sistency, coherency and proof. They like theirknowledge to be well behaved and have devel-oped methodologies to build ontologies cleanlyfrom the top down, from scratch, with good princi-ples. Because they are developing techniques forall applications, their results are expected to begeneric. They have example ontologies but, asthis community typically is concerned with themechanics of the ontology rather than its content,the examples are usually small and pathologicallydesigned to test the boundaries of the expressive-ness of languages or challenge reasoning engines.However, there are some examples of content

efforts from this community. For example, Open-cyc currently has around 60 000 terms describing‘common ideas’ made publicly available from the260 000 or more concepts of the Cyc ontology(http://www.opencyc.org). Despite the fact thatmuch of the work is with application stakeholders,Montagues tend towards ‘technology push’, usingthe application as an experimental sand box dur-ing their pursuance of academic excellence. ThisHouse has been active for around four decadesand during that time have developed a startlingand confusing number of languages — CycL, KL-ONE, RDF, RDFS, OIL, DAML + OIL, OWL,RuleML, SWRL (Gomez-Perez and Corcho, 2002;Horrocks et al., 2004) — and the tools to go withthem — FaCT, RACER, OilEd, Protege, Protege-OWL, OntoBroker, Jena (Denny, 2004). Their e-commerce cousins have added to the mix withTopic Maps, UML, RosettaNet and ebXML. Thisactivity has escalated in recent years, motivated bythe Semantic Web vision, which has led this com-munity to engage intensely with the standardizationactivities of the W3C.

The Semantic Web is an extension of the currentWeb, in which information is given well-definedmeaning, better enabling computers and people towork in cooperation. In practice, this is exposingthe meaning of Web resources by assertions in acommon data model, Resource Description Frame-work (RDF), and the publication and sharing ofconsensually agreed ontologies in RDF Schema(RDFS) or Web Ontology Language (OWL), sothat metadata can be shared and background knowl-edge can be declared. We use this semantic fabricto query, filter, integrate and aggregate the meta-data, and reason over the metadata and ontologiesto infer more metadata. To declare a measure ofconfidence in the assertions and inferences, weattribute trust to the metadata and proof to theinferences. The idea is to create a platform forautomated, computational, sentient agents to oper-ate over. Then these agents can dynamically dis-cover and combine resources and applications onbehalf of users, e.g. to book a medical appointmentor make travel arrangements (Berners-Lee et al.,2001). As a consequence, the Montagues are cur-rently found in the World Wide Web Consortium(W3C) Semantic Web and Semantic Web ServicesActivities. They have also had to become moretolerant of confusion as, unlike their traditionalknowledge 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, andcompeting and conflicting logical claims that makereasoning tricky.

The Capulets

The other, with blessed scientist’s vigour/Acts has-tily on models that endure. This is the House ofthe Life Scientists. The world of bioinformatics isof pragmatics and practice, with a strong applica-tion pull. Their motivating vision is one of well-structured controlled vocabularies for informationsharing, classification and indexing. These are usedto enhance accurate retrieval, create common stan-dards for annotation and support the mediationbetween and interlinking of the contents of dif-ferent databases. Capulets have been classifyinganimals since Aristotle and Linnaeus. Increasingly,ontologies are being used for applications otherthan annotation, such as data mining. Whereasthe Montagues see knowledge representation asan end in itself, the Capulets see it as a meansto an end, and that end is Science. Their oper-ating timescale is immediate; they have a prob-lem now and they are in a hurry. Thus, theirapproach is ‘build it, use it, and fix it later’.Ontologies have typically been seeded from key-word lists or by small groups of highly moti-vated service providers/users. They have been putto use immediately, so there is no futile attemptto ‘get it right first’. Consequently, methods forevolution and change have been present from thestart. Drawing from their legacy of database cura-tion practice, the Capulets have developed work-able methodologies for consensually developingcommunity-wide ontologies, supported by sophisti-cated infrastructure (Bada et al., 2004). The ontolo-gies are specific rather than generic, focusing ongene products, microarray experiments, sequences,anatomy, etc. At the time of writing, 39 were avail-able from the Open Biological Ontologies website(http://obo.sourceforge.net), a gathering place ofthe community. Most are simple in their struc-ture — graphs or taxonomies — but their cover-age, relevance and take-up is significant and real.The favourite child of the Capulets is the GeneOntology, an international effort of over 18 000concepts with wide adoption that has made a sig-nificant impact (GO Consortium, 2000). They arekeen on using standards and tools, but do nothesitate to construct their own if none of those

available is appropriate. The Capulets inhabit anincreasingly crowded landscape as life sciencesmove to systems biology. The medical informat-ics and healthcare community has many ontologiesof its own and increasingly this world needs to belinked to that of genomics.

The Philosophers

One, comforted by its logic’s rigour/Claims ontol-ogy for the realm of pure. The Montagues havecousins. These are also firmly in the realm of the-ory, but whereas the Montagues concentrate on therepresentation of conceptual models of truth thataid an application, the Philosophers seek a singlemodel of truth itself. Some even believe in oneuniversal, unifying ontology. They build founda-tional ontologies such as SUO (the Standard UpperOntology) or DOLCE (a Descriptive Ontology forLinguistic and Cognitive Engineering) (Lehmann,2004), that contain concepts such as ‘perdurant’(an entity that extends in time but is not whollypresent at any one time) and ‘endurant’ (an entitythat is wholly present through time). Their moti-vations are the theories of parts and wholes (i.e.mereology), essence and identity, dependence qual-ities, composition and constitution, participationand representation, which they claim should formthe foundation of all ontologies. However, theircontributions are helpful — they have developedmethodologies and patterns that are relevant to LifeScientists, e.g. when modelling parts and wholes ofbiological structures. They are not usually, how-ever, concerned with earthly pursuits such as tools,and take the high intellectual ground when it comesto academic scholarship. They are also notoriouslyargumentative, not given to building consensus,and have been arguing since Aristotle’s time. Theiroperating timescale is ‘as long as it takes’.

We have three Houses, as shown in Figure 1.Each speaks a different language, each has adifferent agenda, each plays a different role andeach works to a different timescale. In some waysthese differences are beneficially complementary,but they also sow the seeds of conflict.

Duels

From ancient grudge break to new mutiny/When‘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 beunited through the Life Science Semantic Web

hosted the first Bio-Ontologies workshop, whichwas an energetic affair. On the positive side,representatives of the Houses came together; on thenegative side, the divisions between the hardcoremembers became apparent. Notably, a presentationof a foundational bio-ontology commencing withthe concept ‘being’ led a breakaway group ofCapulets, led by Professor Michael Ashburner, tofound the Gene Ontology. Since then there havebeen other duels, usually started by the same oldquarrels. As a service to genomics, here is a guideto the best ways to start an argument.

How to frustrate a Capulet if you are aMontague

• All or nothing. Argue or imply that unlessyou are using all of the expressivity and rea-soning capabilities of a knowledge representa-tion language, e.g. OWL-DL (Horrocks et al.,2003), then you shouldn’t use it at all. A Mon-tague often sees the language features but notthe amount of effort needed to use them. The

cost–benefit proposition for a particular applica-tion may not warrant such an effort; Wroe et al.(2003) outline the work needed to migrate fromthe current Gene Ontology to DAML + OIL.Using a fraction of the expressivity of a languageand adopting a common exchange language hasmajor benefits in itself (Stevens et al., 2003).

• Nine items or less. Present an invented ontologyexample made up of 20 complicated artificialconcepts, using every technical feature available,about wine or Clyde the elephant (a common toyexample used in AI publications).

• Suits me. Offer an ontology tool that is straight-forward if you have a background in logicbut exposes the underlying formalisms in anunintelligible way to a domain scientist. Thissays, ‘if you become like me, you too can usethis’ — which is at best unhelpful.

• Keep still. Present mechanisms that support cre-ation of an ontology from scratch, but do notsupport changes or versioning of either the ontol-ogy itself, or the metadata that uses it. In fact, theresponse ‘that’s a research topic’ to a predictable



problem associated with an ontology as a con-sequence of it being living and working will begreeted with a sigh. Ontologies are conceptual-izations of consensual knowledge of a commu-nity — the consensus changes, the communitiesevolve, and the conceptualizations change.

• We know best. ‘Tell us what you want to sayand we will build it for you’. Twenty yearsof work on knowledge elicitation and knowl-edge acquisition lead to a view that a ‘sub-ject matter expert’ tells their knowledge to aknowledge engineer, who then encodes it. Inthe complex world of molecular biology researchand related disciplines, where knowledge is thepoint of the scientific endeavour, this is atbest a conceit. In the Halo project experiment(http://www.projecthalo.com/), analysis of themistakes in the ontologies built showed them tobe misunderstandings and simplifications by theknowledge engineers.

• The finished product. Refuse to release the ontol-ogy until it is ‘finished’, and when it is finishedbe astonished that the users won’t use it. Thisis often because the ontology is designed interms of the knowledge engineer, not the domainexpert. Until surprisingly recently, the knowl-edge management community concentrated onthe early parts of the knowledge life cycle,neglecting maintenance, in particular contin-ued distributed development by a large numberof knowledge contributors. Even today, manyknowledge acquisition tools remain unconnectedto ontology editors. Honourable exceptions exist,such as the GALEN tools for clinical ter-minology development — we observe that thiswas an application-driven project (Rogers et al.,1997). Protege-OWL, part of the Co-ode project(http://www.co-ode.org), aims to eliminate theknowledge engineer middleman from the knowl-edge acquisition process and support domainexperts to accurately and effectively build theirown ontologies, drawing on the GALEN experi-ences.

• Not my problem. Offer to solve a different prob-lem than the one actually presented. Maybe theproblem can be dealt with by a sociological solu-tion, which hardly ever attracts a Montague’sinterest but is feasible by well-organized com-munities of curators and knowledge contributors.Often a Montague would rather solve a harderproblem (that to them is more interesting and

fun), than take a simpler ‘good-enough’ route.At the heart of this lies the different agendas ofthe two Houses: Capulets want to link togetherscientific data well enough to get on with sci-ence, whereas Montagues want to build sentientapplications.

How to frustrate a Montague if you are aCapulet

There are two sides to every story:

• Repeat the same old mistakes. Make the samemistakes and the same misunderstandings, overand over again. Montagues have a wealth ofexperience in modelling, e.g. in mereology (Win-ston et al., 1987) and the differences betweeninstances and concepts (Noy and McGuinness,2001). They know that simple approaches usingdirected acyclic graphs do not gracefully scale.Manually predetermining and classifying everycombination of every term is unnecessary andunsafe when logic languages automatically offerassistance (Rogers et al., 1998). Simplificationsmade early on in the development of an ontol-ogy, for understandable reasons, store up trou-ble down the road that can be foreseen if oneis willing to pay the cost now rather thanlater. For example, combinatorial explosion ofmetabolic processes in the Gene Ontology even-tually becomes difficult to maintain by hand andwill lead to incompleteness in the structure andreduced performance in its intended databaseretrieval task (Wroe et al., 2003).

• It works. Hack together a mechanism, tool orapplication and declare it to work (with no evi-dence what that means) for a specific example,with no guarantee that it will work with anyother data. Montagues are driven by genericsolutions that are explainable, repeatable, sus-tainable and independent of freaks of data. Theyabhor baroque solutions with a large ‘exceptions’case load.

• Ignorance is bliss. Ignore the past four decadesof reasoning and knowledge representation rese-arch, along with the understanding of the bound-aries of expressivity of languages and the algo-rithms that infer knowledge using them. A lan-guage with every construct in it, like OWL-Full,is not decidable. Error tolerance is not the same



as ignorance of errors and inconsistencies. Just asour understanding of the genome has advancedastonishingly over the past decade, so astonish-ing advances have been made in computing inunderstanding of the decidability and tractabilityof expressive knowledge languages.

• I know what it means — but that doesn’t meanthat anyone or anything else will. Machine-computable ontologies need formal and explicitsemantics. A shared common understandingrequires unambiguously specified clear seman-tics.

• I tried your software and it broke/stopped res-ponding, so I went off and built my own. Themajority of life science ontologies are large; theGene Ontology currently stands at 18 000 con-cepts. These are much larger than the examplesused to develop the Montagues’ software, whichin the past has struggled to cope. Scalability andperformance do matter but are not usually thetop priority for the Montagues.

• Keep it simple. A little semantics goes a longway; what a waste of the language when its rich-ness is ignored! There is plenty of complexity tobe had if it can be managed, but a Capulet willnot make something complex if simple workswell enough.

• Consensus outweighs complexity. The realiza-tion that no matter how simply structured orscruffy the ontology, what counts is if every-one uses it, e.g. the SOFG Anatomy Entry List(http://www.sofg.org/sael/) is a just a list of fewhundred terms but enough to bind a community.Similarly, it doesn’t matter how smart and soundand complete the ontology is, if no one uses it, itdoesn’t count. To date, it is hard to find a ‘smart’ontology that has made it into widespread use.SNOMED-RT, and its successor SNOMED-CT,is smart on the inside for maintenance purposes(Spackman et al., 1997), but goes through a pro-cess of semantic materialization — which turnsit into graphs — to make it appear simple tohealthcare applications and users.

How to frustrate a Montague or aCapulet if you are a Philosopher

The Philosophers have centuries of modellingexperience and thought but can comfortably startan interminable argument with all and any of

the Houses. The issue comes down to practicalengagement with those actually building the ontol-ogy.

• Finger pointing from the sidelines. Declare thatthe hard working ontology curators are not doingit right but do not tell them why, or give themany practical tools or guidance. Do not tell themdirectly, using their public curation policies andmechanisms, but instead make public statementsor whisper in back rooms.

• I wouldn’t start from here. Declare that theontology should be started again, ignoring theeffort needed and its extensive legacy, and offerno migration path for this legacy.

• Mismatched expectations. Complain that theontology is not a model of true knowledge ordoes not fit a different purpose to the one forwhich it was developed. An ontology developedto annotate database entries is not intended to bea complete model of ‘truth’ and may well notsuit an alternative application.

• Truth and beauty. Declare that ‘truth’ is moreimportant than practicality. In reality, consensusis more important than truth and perfection isthe enemy of the good. What is truth? Germantaxonomists considered the giant panda to be abear; British taxonomists that it was a racoon;and American taxonomists didn’t know which itwas. Now it is defined as a bear — because thecommunity has agreed on the definition of a bearand state it is a bear, not because it is the ‘truth’.

• There are no such things as concepts. Take anesoteric stand that might win accolades in ajournal of philosophy but is unhelpful in practice.

Balcony scenes

From forth the fatal loins of these two foes/Resear-chers to unlock the book of life;/Whole misadven-tured piteous overthrows/Can with their work burytheir clans’ strife. Amid these arguments there arewonderful examples of reconciliation and mutualsupport. Many Capulets and Montagues are work-ing closely together, and each complements theother, as we show in Figure 2.

Using the W3C RDF/OWL standards

Both houses are in favour of standards — usingthem 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 ontologylanguage, OWL (Horrocks et al., 2003), for theSemantic Web that draws from their years ofresearch and practice. They have also produced

the RDF language for describing assertions usingontology terms. The Capulets have proved to beenthusiastic early adopters of these languages. TheOpen Biological Ontologies (http://obo.sourcefor-



ge.net/) consortium mandates OWL as one ofits preferred exchange languages; new ontologies,such as BioPAX (http://www.biopax.org/), areusing OWL from the outset. Other work on theGene Ontology uses language processing and pat-terns to extract implicit knowledge within it anduses reasoning to identify additional subsump-tion relationships and inconsistencies (Wroe et al.,2003).

The Age of Reasoning

Hand in hand with the adoption of OWL is themovement towards the judicial use of reasoningnecessary to support the scale of the ontologiesneeded by the community. This includes figuringout when reasoning provides the best benefitsin the ontology life cycle. Modelling using acompositional, term coordination approach (insteadof pre-enumerating and classifying every term byhand) and a stronger emphasis on relationshipsbetween concepts motivates a need for reasoning,but reasoning is not the be-all and end-all.

Tools

We see a convergence of ontology creation toolsas the ontologies become more sophisticated, yetthis complexity needs to be simplified for the user.OBO-Edit (https://sourceforge.net/project/show-files.php?group id=36 855) is becoming more likethe full Protege-OWL editor (Knublauch et al.,2004), which in turn is adopting wizards andplug-ins to simplify and specialize interaction withthe ontologies, like OBO-Edit. Tools like XSPAN(http://www.xspan.org/), developed for the lifescience community, can contribute more generally.The challenge is to lower the barriers of entry fordevelopers and knowledge contributors, and pro-vide ‘invisible’ tooling for end applications. Weneed tooling for vocabulary management and appli-cation developers, rather than core ontology devel-opment. We need scalable, efficient reasoning andRDF stores capable of dealing with millions ofassertions.

Ontology patterns

Presumably to the Philosophers’ delight, the Capu-lets are turning to mereology for the more princi-pled and systematic representation of taxonomies

[e.g. in the Sequence Ontology (http://song.sour-ceforge.net/) and the Foundational Model of An-atomy (http://sig.biostr.washington.edu/projects/fm/)] and expanding to relationships other than is-aand part-of [e.g. in the Chemical Entities of Bio-logical Interest dictionary (http://www.ebi.ac.uk/chebi/)]. Relationships in biology do not have thebaggage that concepts have, and so represent afresh opportunity for bio-ontologists.

Methods

The Gene Ontology effort has created a goldstandard method for community-wide consensualdevelopment of a working and living ontology(Bada et al., 2004). Given that this works in prac-tice, it is reassuring that more or less the sameapproach has now been proposed by the Montaguesin the DILIGENT methodology (Pinto et al., 2004),and thereby works in theory too. Incremental evolu-tion is a day-to-day occurrence dealt with by highlycurated ontologies like GO, and by such method-ologies. Paradigm revolutions — where currentscientific orthodoxy is overthrown, requiring a newontology that is not an incremental extension of theold but is incompatible with the old — are less wellsupported, and a present a major technical and soci-ological challenge. If the effort to support paradigmshifts is too large we are in danger of fossilizingour knowledge. Experiences from the life scienceshave shown that the success of large-scale ontol-ogy building is more sensitive to social or politicalprocesses than technology.

Marriage or poison?

So our scene is laid. Can we help each otherto shelter from the maelstrom of standards, lan-guages, prototypes, tools, content and commercialofferings? Is a marriage between our Houses pos-sible? One such bond is the Semantic Web ini-tiative. It has motivated the Montagues to pro-duce standard languages for ontologies and totackle issues they had previously ignored, suchas incomplete and inconsistent knowledge. TheCapulets have already reaped the benefits. TheWeb needed incubation in a friendly, containedand forgiving community with a true distributedinformation problem (physics); the Semantic Webwould benefit from the same and the ideal bed-fellow is life sciences. In October 2004 W3C



hosted the first Semantic Web for Life Sci-ences meeting (http://www.w3.org/2004/07/swls-ws.html), which attracted over 100 delegates.Semantic Web technologies such as RDF andOWL, with domain-specific standards like LSID(Clark et al., 2003), represent an evolvable, inter-operable and fundamentally network-driven appro-ach to information and knowledge aggregation. Assuch, they appear to represent an opportunity fordeveloping solutions to overcome some of the diffi-cult technology issues in life sciences — disparate,constantly evolving data sources and ontologies (inboth public and private settings) and the need toaggregate the data and ontologies into a resourcethat can be queried, securely, and result in an audittrail.

However, this marriage could become poison forthe Capulets if it is not entered into as an equalpartnership. For example, during standardizationprocess of OWL-DL, the expressivity to describequalified number restrictions — the ability to saythat a normal hand has five fingers and oneof these must be a thumb — was abandoned.The technical know-how is well understood; it islogically expressible and tractable for reasoning. Itisn’t there because the W3C standards committeedid not fully realize its obvious crucial importanceto the life science community until it was toolate, and the community did not voice its concernsclearly and loudly. The W3C Semantic Web BestPractice Activity has taken care not to make thesame mistake. The Capulets must ensure that theirinterests are being served and that they are notmerely an expedient test case for the Montagues.That means they must engage with them andtheir language design efforts, their standardizationactivities and their tool building.

Romeo and Juliet is a tragedy. How do we turnour story into a happy ending? Here is a desideratafor a good marriage. Collaboration through Con-versation; set aside the time and patience to over-come the language barriers. Hold mutual Respectand Understanding for the other’s motivations andcontributions. Avoid being judgemental. Compro-mise should be viewed as a success and not afailure. Work as a Partnership. Take a look ateach duel point and think about it. Do you saythat? Do you hear that? How can we benefit fromeach other’s experience and results?

As the Prince of Genomics might say (echoingthe Prince of Verona; our peacekeepers are those

who bridge all communities, typically originallytrained in medical or life sciences, such as AlanRector and Mark Musen):

Rebellious subjects, enemies to peaceThrow down your mistemper’d weapons to the groundGo hence, to have more talk of these top ‘things’;All should be understood, respect’d, and well-found:For never was a story with more GOThan this of AI, life science and the O.

Acknowledgements

We would like to thank Robert Stevens, Chris Cattonand William Shakespeare for ideas that contributed to thisarticle and the SOFG talk that originated it.

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