Werner CEUSTERS, MD Center of Excellence in Bioinformatics and Life Sciences

1. Clinical Trial Ontology MeetingHow to build an Ontology ?Some basic principlesNIH, May 16-17, 2007 Werner CEUSTERS, MD Center of Excellence in Bioinformatics and Life Sciences Department of Psychiatry, University at Buffalo, NY, USA http://www.org.buffalo.edu/RTU

2. 1959 - 2006 Short personal history 1977 2004 1989 1992 2002 1998

3. Mainstream interpretations of “ontology” • An explicit specification of an agreed upon conceptualization of a domain • Tom Grüber • Anything what is given the name ‘ontology’ and that can be described in terms of 6 axes: expressiveness, structure, intended use, granularity, automated reasoning, prescriptive/descriptive • Ontology Summit 2007

4. X part of Y Problems with mainstream ontologies • Based upon the confusing notion of “concept” • Unit of thought or knowledge concerning anything perceivable or conceivable • The meaning of a term • … • Confuse information representation with domain representation Information about X part_of information about Y

5. What I mean with the word “ontology” • A representation of some pre-existing domain of reality (a portion of reality) which • reflects the properties of the entities within its domain in such a way that there obtains a systematic correlation between reality and the representation itself, • is intelligible to a domain expert • is formalized in a way that allows it to support automatic information processing reality

6. Three levels of reality • The world exists ‘as it is’ prior to a cognitive agent’s perception thereof; • Cognitive agents build up ‘in their minds’ cognitive representations of the world; • To make these representations publicly accessible in some enduring fashion, they create representational artifacts that are fixed in some medium. Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. Proceedings of KR-MED 2006, November 8, 2006, Baltimore MD, USA

7. B Cognitive representation RU1B1 RU1O1 concretization O R 1st level reality Represent what exist and is relevant

8. Some characteristics of representational units • each unit is assumed by the creators of the representation to be veridical, i.e. to conform to some relevant POR as conceived on the best current scientific understanding; • several units may correspond to the same POR by presenting different though still veridical views or perspectives; • what is to be represented by the units in a representation depends on the purposes which the representation is designed to serve.

9. Some characteristics of an optimal ontology • Each representational unit in such an ontology would designate • (1) a single portion of reality (POR), which is • (2) relevant to the purposes of the ontology and such that • (3) the authors of the ontology intended to use this unit to designate this POR, and • (4) there would be no PORs objectively relevant to these purposes that are not referred to in the ontology.

10. Three types of ontologies • Upper level ontologies: • (should) describe the most generic structure of reality • Domain ontologies: • (should) describe the portion of reality that is dealt with in some domain • Special case: reference ontologies • Application ontologies: • To be used in a specific context and to support some specific application

11. Clinical trial ontologies • As domain ontologies: • Cover all entity types relevant in the clinical trial domain • As application ontologies: • A subset of the above which is large enough to support all functions the application has to serve: • CT protocol development • Study management • Data analysis • …

12. Key question How to build an optimal clinical trial domain ontology ?

13. Rule 1: Analyze the domain

14. Rule 2a: Try to be lazy: re-use what others have done.

15. The BRIDG (domain analysis) model • NOT an ontology • A computable clinical trials protocol representation • that supports the entire life-cycle of clinical trial protocols, and • that will serve as a foundation for caBIG modules • that support all phases of the clinical trials life cycle, (including protocol authoring) and • be developed to meet user needs and requirements. • The BRIDG Project: Creating a model of the semantics of clinical trials research. Douglas B. Fridsma. July 26, 2006

16. Reasons for selecting BRIDG • BRIDG tries to solve an important problem • Does not completely ignore reality as many other initiatives do: • If the tools and models don’t work with reality, it is probably the tools and the models that need to change • The BRIDG Project: Creating a model of the semantics of clinical trials research. Douglas B. Fridsma. July 26, 2006 • Intended to become the next best thing on earth (after HL7, I assume) (although one has to search hard to find evidence and sometimes it looks as if some contributors observed reality from outer space)

17. http://www.bridgproject.org/status.html

18. BRIDG_Model_V1_49

19. BRIDG model organization Image from: The BRIDG Project: Creating a model of the semantics of clinical trials research. Douglas B. Fridsma. July 26, 2006

20. Rule 2b: Try to be lazy: re-use what others have done, But… remain critical at all times!

21. Being critical ≠ being negative RFQ-NCI-60001-NG: Review of NCI Thesaurus and Development of Plan to Achieve OBO-Compliance Grant to Apelon (H. Solbrig) to improve NCIT

22. Rule 3: Don’t have a blind trust in the power of representation and modeling languages, and certainly not in UML

23. ‘Death by UML Fever’ • It is important to emphasize that UML itself is not the direct cause of any maladies described herein. • Instead, UML is largely an innocent victim caught in the midst of poor process, no process, or sheer incompetence of its users. • UML sometimes does amplify the symptoms of some fevers as the result of the often divine-like aura attached to it. • For example, it is not uncommon for people to believe that no matter what task they may be engaged in, mere usage of UML somehow legitimizes their efforts or guarantees the value of the artifacts produced. Alex E. Bell. Death by UML Fever. Queue 2(1), March 2004, ACM Press, 72 – 80, 2004

24. Who would not be impressed ? • Fig. 10: BRIDG Comprehensive Class and attribute diagram - (Logical diagram), p99

25. I’m not ! • I have come to appreciate domain modeling in UML as an implementation-independent approach which is more likely to uncover “the truth” about the underlying semantics. • Dr. Diane Wold. Modeling Trial Design with BRIDG. July 26, 2006 • The UML diagram helped us to keep separate an activity, which exists independent of any schedule, and an activity-at-a-visit, (the X), which is a plan to perform that activity at a particular time.

26. Rule 4: Limit the number of developers/contributors

27. Contributors to the BRIDG model A chain is as strong as its weakest link Image from: The BRIDG Project: Creating a model of the semantics of clinical trials research. Douglas B. Fridsma. July 26, 2006

28. Rule 5: Be consistent in what you describe: either representational units, or the entities represented by them. Thus: keep the levels of reality all the time in mind

29. LivingSubject (BRIDG logical model p1031) • Type: Class • Status: . Version . Phase . • Package: Entities and Roles Keywords: • Detail: Created on 02/09/2006. Last modified on 02/09/2006. • GUID: {7C04F8D8-30B9-4942-B2A8-4CF93E8913D9} • An object representing an organism or complex animal, alive or not. Examples: person, dog, microorganism, plant of any taxonomic group, tissue sample, bacteria, fungi, and viruses.

30. SubstanceAdministration (BRIDG logical model p84) • Type: Class PerformedActivity • Status: Proposed. Version 1.0. Phase 1.0. • Package: CTOM Elements Keywords: • Detail: Created on 01/05/2005. Last modified on 12/14/2006. • GUID: {2289C0E8-855D-42e3-86FA-2ECBE59D8982} • The description of applying, dispensing or giving agents or medications to subjects.

31. Person (BRIDG logical model p106 a.f., HE!) • Type: Class • Status: Proposed. Version 1.0. Phase 1.0. • Package: Clinical Research Entities Keywords: • Detail: Created on 06/09/2005. Last modified on 01/13/2007. • GUID: {6F49F110-7B36-4c03-A7EA-F456CE1E739D} • A human being.

32. Some Person Attributes • administrativeGenderCode (p107) • The classification of the sex or gender role of the patient. Values include: Female, Male, and Unknown. • genderCode (p108) • The text that describes the assemblage of physical properties or qualities by which male is distinguished from female; the physical difference between male and female within a person. [Explanatory Comment: Identification of sex is usually based upon self-report and may come from a form, questionnaire, interview, etc.]

33. A better example:Clinical Trial Ontology under DOLCE Crenguta Bogdan, Daniela Luzi, Fabrizio L. Ricci, Luca D. Serbanati. Towards a Clinical Trial Ontology using a Concern-Oriented Approach. W.P. n. 10, October 2006.

34. Rule 6: Use a Realism-based Upper Ontology to classify the representational units in your Domain Ontology

35. Realism in Basic Formal Ontology (BFO) • The world consists of • entities that are • Either particulars or universals; • Either occurrents or continuants; • Either dependent or independent; and, • relationships between these entities of the form • <particular , universal> e.g. is-instance-of, • <particular , particular> e.g. is-member-of • <universal , universal> e.g. isa (is-subtype-of) Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. Proceedings of KR-MED 2006, November 8, 2006, Baltimore MD, USA

36. Only what exists (or existed) can be represented • Anything else can be imagined • Examples of what exist: • Body parts • Disorders • Abortions • Women with prevented abortions • Plans about my future activities • What does not exist • Prevented abortions • My future activities

37. PlannedActivity (BRIDG logical model p202, HE!)

38. Rule 7: Use formal ontological methods to: distinguish distinct entities assess in what way distinct entities are distinct

39. Organism (BRIDG logical model p160, HE!) • Type: Class • Status: Proposed. Version 1.0. Phase 1.0. • Package: Clinical Research Roles Keywords: • Detail: Created on 12/13/2006. Last modified on 01/19/2007. • GUID: {B9F321DB-365F-4155-B8F6-3D…. • The role that a biological entity has, and that role participates in a microbiology test in two ways: first, it can be identified as the result of a microbiology test. It can also participate as a specimen in the microbiology test. [HL7 Perspective]

40. An example: ONTOCLEAN • Identity, essence, unity, dependence C. Welty, N. Guarino"Supporting ontological analysis of taxonomic relationships", Data and Knowledge Engineering vol. 39, no. 1, pp. 51-74, 2001

41. Rule 8: Don’t confuse reality with our means to access that reality, f.i.: Don’t confuse the observation of an entity with the entity observed

42. AdverseEvent (BRIDG logical model p168, HE!) • Type: Class Assessment • Status: Proposed. Version 1.0. Phase 1.0. • Package: Clinical Research Activities Keywords: • Detail: Created on 05/24/2006. Last modified on 01/26/2007. • GUID: {CD620136-3CB9-4382-802B-F6CA82F98C10} • An observation of a change in the state of a subject that is assessed as being untoward by one or more interested parties within the context of protocol-driven research or public health.

43. Example: medical ‘findings’ and ‘observations’ • A particular pathological entity may at a certain time be undetectable by any observation method or technique available to an observer, including the person exhibiting the pathological entity itself.

44. Example: medical ‘findings’ and ‘observations’ (1) • A particular pathological entity may at a certain time be undetectable by any observation method or technique available to an observer, including the person exhibiting the pathological entity itself. • A particular observation (‘act of looking’) may produce false results and thus simulate the existence of a pathological entity.

45. Example: medical ‘findings’ and ‘observations’ (1) • A particular pathological entity may at a certain time be undetectable by any observation method or technique available to an observer, including the person exhibiting the pathological entity itself. • A particular observation may produce false results and thus simulate the existence of a pathological entity. • An observer may observe or fail to observe a detectable particular pathological entity.

46. On ‘findings’ and ‘observations’ (2) • When an observer perceives a particular pathological entity, he might judge it • (1) to be an instance of the universal of which it is indeed an instance in reality, • (2) to be an instance of another universal (and thus be in error), or • (3) he might be not able to make an association with any universal at all. • Distinct manifestations of ‘the same type’ may be pathological or not: • Singing naked under the shower versus in front of The White House • ...

47. Rule 9: Do not accept silly suggestions, whomever they come from

48. Device (BRIDG logical model, p100, HE!) • Type: Class Material • Status: Proposed. Version 1.0. Phase 1.0. • Package: Clinical Research Entities Keywords: • Detail: Created on 02/22/2006. Last modified on 01/04/2007. • GUID: {3546A977-C51F-4860-A09A-2ADAE896D74B} • <PROPOSED>A therapeutic or diagnostic intervention utilizing a piece of equipment or a mechanism designed to serve a special purpose or perform a special function whose basic characteristics are not altered in the course of the intervention.

49. The latter could also go under other rules: • Stop working when you are tired • Be careful with cut and paste • Proof-read your work • …

50. Rule 10: Use distinct names for distinct representational units that denote distinct entities