1. www.landc.be 1/39 Terminology and Ontology Management Systems Dr. W. Ceusters CTO Language & Computing nv

2. www.landc.be 2/39 Overview Terms and definitions The Semantic Web: sense and nonsense L&C’s approach to Semantics Assisted Knowledge Management, concentrating on: –“formal” ontology management –relationship with language –Software support Examples in healthcare Conclusions

3. www.landc.be 3/39 “Terminology” “set of terms representing the system of concepts of a particular subject field.” (ISO 1087:1990) “a theory, i.e. the set of premises, arguments and conclusions required for explaining the relationships between concepts and terms.” (Sager 1990)

4. www.landc.be 4/39 “Ontology” In Information Science: –“An ontology is a description (like a formal specification of a program) of the concepts and relationships that can exist for an agent or a community of agents.”(Tom Gruber) In Philosophy: –“Ontology is the science of what is, of the kinds and structures of objects, properties, events, processes and relations in every area of reality.” (Barry Smith)

5. www.landc.be 5/39 Ontology through the ages 350 BC: –Aristotle ‘first philosophy’  ‘metaphysics’  ‘Ontology’ 1613: –Rudolf Göckel (Goclenius) Lexicon philosophicum –Jacob Lorhard (Lorhardus) Theatrum philosophicum 1721: –Bailey’s dictionary defines ontology as ‘an Account of being in the Abstract’. 1964: –Ingarden ‘ontology’ = the study of what might exist ‘metaphysics’ = the study of which of the various alternative ontologies proffered is true of reality.

6. www.landc.be 6/39 Terminology, Ontology and Logic “Ontologies are not limited to conservative definitions, that is, definitions in the traditional logic sense that only introduce terminology and do not add any knowledge about the world.” (Herbert Enderton, 1972) Main additional requirement: –one needs to state axioms that do constrain the possible interpretations for the defined terms

7. www.landc.be 7/39 Ontology and Language “The subject of ontology is the study of the categories of things that exist or may exist in some domain. The product of such a study, called an ontology, is a catalog of the types of things that are assumed to exist in a domain of interest D from the perspective of a person who uses a language L for the purpose of talking about D.” (John Sowa)

8. www.landc.be 8/39 From buzz-word to the “O-word” “An ontology is a classification methodology for formalizing a subject's knowledge or belief system in a structured way. Dictionaries and encyclopedias are examples of ontologies.” (X1) “A terminology (or classification) is a kind of ontology by definition and it should preserve (and "understand") the relationships between the 1,000s of terms in it or else it would become a mere dictionary (or at best a thesaurus).” (X2) “Ontologies are Web pages that contain a mystical unifying force that gives differing labels common meaning.” (X3)

9. www.landc.be 9/39 Amen !!! “Give folks a loose standard and the first thing many of them do is exploit its weaknesses for their personal gain.” NICHOLAS PETRELEY Computerworld “Give folks a loose standard and the first thing the clever ones do is exploit the ignorance of the others for their personal gain.” WERNER CEUSTERS (in a vicious mood)

10. www.landc.be 10/39 "Where there is the sound of a blow, there is respect” (Pashtun proverb) “I repeatedly get confused by the (in my opinion structurally confusing) terminology of those people (like Y) who try to do ontology but end up just studying concepts.” (X, pers. comm.)

11. www.landc.be 11/39 The basics are indeed confusing enough Nth-order Universal Particular Individual Universal Particular Concept Instance Individual Set Universal GuarinoHegelSmith“Conceptualists” Conceptualisation Real world

12. www.landc.be 12/39 IT or philosophy: does it matter ? Does what I see exist ? –hallucinations, illusions,... The Matrix What is the relationship between me, my life and my body ? If X IS-A Y, does it need to be a Y ? –Cfr. Y = “person” versus Y = “nurse” If X stops to be a Y, does it stop being ? –Cfr. Y = “person” versus Y = “nurse”

13. www.landc.be 13/39 Does it matter ? The answer is YES as many philosophical questions have proven to be the only way to build clean ontologies

14. www.landc.be 14/39 Tim Berners Lee: “I had a dream “

15. www.landc.be 15/39 Ontologies and the semantic web The goal of the Semantic Web is to make it possible for software to find the data it needs on the Web, understand it, cross-reference it and apply it to a particular task. “I should be able to tell my Web-enabled handheld device to schedule an appointment with a dentist within 20 miles of home and let the computer do the rest.” (X3)

16. www.landc.be 16/39 If it were just that simple... “I should be able to tell my Web-enabled handheld device to schedule an appointment with a dentist within 20 miles of home and let the computer do the rest.” So the SW must understand natural language ? So the SW must know when the requester is free ? So the SW must understand that it is to take care of the requester’s teeth, and not to have a nice diner date ? So the SW can then deduce what the actual length of “20 miles” is for this particular person ? So the SW must understand where the requester lives ?     

17. www.landc.be 17/39 The solution... Build one common ontology. Use precise, unambiguous terms to name the concepts in the ontology. Annotate webpages by using this ontology. Train people in using the terms in the same sense as understood by the ontology.... is an extremely naïve solution !

18. www.landc.be 18/39 This is (a piece) of the reality... Computers don’t understand natural language (yet). Web pages are in free text. Manual ontological mark-up of web pages is unfeasable. No single, common ontology will ever exist ! Nobody can make humans to use terms always and ever in the same way.

19. www.landc.be 19/39 Pray your computer isn’t Irish... X:“Hallo stranger, you appear to be traveling?” Y:“Yes, I always travel when on a journey.” X:“And pray, what might your name be?” Y:“It might be Sam Patch, but it isn't.” X:“Have you been long in these parts?” Y:“Never longer than at present—5 feet 9.” X:“Do you get anything new?” Y:“Yes, I bought a new whetstone this morning.” Copyright © 1996 Electronic Historical Publications

20. www.landc.be 20/39 L&C’s approach to Semantics Assisted Knowledge Management

21. www.landc.be 21/39 Mission of L&C nv To provide users and developers of systems for knowledge management with tools and services for efficient and accurate data-entry and retrieval by exploiting the full power of automated (medical) natural language understanding We hereby declare...

22. www.landc.be 22/39 AnthemMulti-TaleDomeGIUSelectC-CareLiquidMobidev R/D ratio

23. www.landc.be 23/39 L&C’s integrated approach Data structure and function library for language understanding Medical and linguistic knowledge required for language understanding NLU enabling tools for knowledge supported data-entry and -retrieval

24. www.landc.be 24/39 L&C’s LinkFactory Linguistic-semantic Function Library C-DEFINE(c-meningitis, c-inflammation HAS-LOC c-meninges) T-DEFINE(“méningite”, french, c-meningitis) Storage Functions Retrieval Functions GET-TERMS(c-meningitis, {french, dutch}) “méningite”, “hersenvliesontsteking”

25. www.landc.be 25/39 Architectural Overview

26. www.landc.be 26/39 Client Graphical Objects

27. www.landc.be 27/39 Build-in Quality Control Knowledge entered is immediately used to check validity of subsequent entries Version management User-management with : –Allowed actions based on experience –Personal audit trail Clear and formal separation with 3 rd party systems to avoid copying mistakes such as: –UMLS’ cyclical ISA relationships –SNOMED-RT ‘s “very usual = always” modelling –Most systems’ overloaded hierarchical relations

28. www.landc.be 28/39 The content Formal Domain Ontology Lexicon Grammar Language A Lexicon Grammar Language B Cassandra Linguistic Ontology MEDRA ICD SNOMED ICPC Others... Proprietary Terminologies

29. www.landc.be 29/39 Based on formal logics HAS- PARTIAL- SPATIAL- OVERLAP IS- TOPO- INSIDE- OF IS-GEO- INSIDE- OF IS- INSIDE- CONVEX- HULL-OF IS-PARTLY- IN-CONVEX- HULL-OF IS- OUTSIDE- CONVEX- HULL-OF HAS- DISCONNECTED- REGION HAS- EXTERNAL- CONNECTING- REGION HAS-DISCRETED- REGION HAS- TANG.- SPAT.- PART HAS-NON- TANG.- SPAT.- PART IS- SPAT.- EQUIV.- OF IS- TANG.- SPAT.- PART-OF IS-NON- TANG.- SPAT.- PART-OF HAS- PROPER- SPATIAL -PART IS- PROPER- SPAT.- PART-OF HAS- SPATIAL -PART IS- SPATIAL -PART- OF HAS- OVERLAPPING -REGION HAS- CONNECTING- REGION HAS-SPATIAL- POINT- REFERENCE

30. www.landc.be 30/39 Example: joint anatomy joint HAS-HOLE joint space joint capsule IS-OUTER-LAYER-OF joint meniscus –IS-INCOMPLETE-FILLER-OF joint space –IS-TOPO-INSIDE joint capsule –IS-NON-TANGENTIAL-MATERIAL-PART-OF joint joint –IS-CONNECTOR-OF bone X –IS-CONNECTOR-OF bone Y synovia –IS-INCOMPLETE-FILLER-OF joint space synovial membrane IS-BONAFIDE- BOUNDARY-OF joint space

31. www.landc.be 31/39 Linguistic and domain ontologies Having a healthcare phenomenon Generalised Possession Healthcare phenomenon Human IS-A Has- possessor Has- possessed Patient Is-possessor-of Patient at risk IS-A Has-Healthcare- phenomenon Risk Factor IS-A Is-Risk- Factor-Of Patient at risk for osteoporosis Risk factor for osteoporosis Osteoporosis Has-Healthcare- phenomenon Is-Risk- Factor-Of IS-A 1 1 1 2 2 3 3 4 4

32. www.landc.be 32/39 Linking external ontologies MESH-2001 : “Seizures” MESH-2001 : “Convulsions” Snomed-RT : “Convulsion” Snomed-RT : “Seizure” L&C : ConvulsionL&C : Seizure L&C : Health crisis L&C : Epileptic convulsion IS-A IS-narrower-than ISA Has-CCC

33. www.landc.be 33/39 Status of LinkBase per 01-12-2002 920.000 (850.000) concepts 2.300.000terms 320link-types 3.000.000link instances 300.000links to 3 rd party systems But: –Never finished ! –Quality sufficient for current applications

34. www.landc.be 34/39 Linguistic Application Components Text ResultProcessor Domain representation Goal representation LinguisticKnowledge TaskKnowledge Formal domain ontology

35. www.landc.be 35/39 Some available components Coding tools: FastCode Semantic indexers: Tessi Spell checkers and type ahead: FastType Semi controlled language parsers in restricted domains: FreePharma Ontology browser Stochastic dependency-based indexer: C-Link (Ir)relevant document classifier for very low prevalence data sets

36. www.landc.be 36/39 Automated application building FastCode Generator LinC- Factory Formal representation of Classification system LinCBase Mapping data Domain+Linguistic ontology FastCode client FastCode server Coding data

37. www.landc.be 37/39 Ontologies for Semantic document management User query Document collection Document(s) retrieved indexing Topic List Index Document Topic assignment Q-analyser Q-matcher Domain ontology LinkFactory TeSSI FastCode FastType QBuilder

38. www.landc.be 38/39 Key principles of success Clean separation of knowledge (adapted from A. Rector) but with close interoperability (W. Ceusters) Conceptual knowledge: the knowledge of sensible domain concepts Knowledge of definitions and criteria: how to determine if a concept applies to a particular instance Surface linguistic knowledge: how to express the concepts in any given language Knowledge of classification and coding systems: how an expression has been classified by such a system Pragmatic knowledge: what users usually say or think, what they consider important, how to integrate in software

39. www.landc.be 39/39 Conclusion Traditional approaches to knowledge management are insufficient Formal terminologies provide: –better QA methods for developing “semantics aware” systems, especially for multi-lingual use –better ways to have them used by machines rather than people Formal ontologies are candidates to become the new pilars in IT when a number of criteria are satisfied –Accept language as a medium of communication, but be independent of any specific language –Multi-lingual –Domain-oriented –Supported by a methodology, services and tools They are not a goal, but a means !