Formal Ontology and Information Systems

1. Formal Ontology and Information Systems • Barry Smith • http://ifomis.de

2. Institute for Formal Ontology and Medical Information Science • (IFOMIS) • Faculty of Medicine • University of Leipzig • http://ifomis.de

3. The Idea • Computational medical research • will transform the discipline of medicine • … but only if communication problems can be solved

4. Database standardization • is desparately needed in medicine • to enable the huge amounts of data • resulting from trials by different groups • to be fused together

5. How make one system out of all of this? • How resolve incompatibilities? • “ONTOLOGY” = the solution of first resort • (compare: kicking a television set) • But what does ‘ontology’ mean? • Current answer: a collection of terms and definitions satisfying constraints of description logic = application ontology

6. Description logic • a decidable logic (thus much weaker than first-order predicate logic) for manipulating hierarchies of concepts/general terms)

7. Enterprise Ontology • A Sale is an agreement between two Legal-Entities for the exchange of a Product for a Sale-Price. • A Strategy is a Plan to Achieve a high-level Purpose. • A Market is all Sales and Potential Sales within a scope of interest.

8. Gene Ontology • Molecular Function Ontology: tasks performed by individual gene products; • examples: transcription factor,DNA helicase • Biological Process Ontology: broad biological goals accomplished by ordered assemblies of molecular functions; • examples: mitosis,purine metabolism • Cellular Component Ontology: subcellular structures, locations, and macromolecular complexes; • examples: nucleus, telomere

9. Example from Molecular Function Ontology • hormone ; GO:0005179 • %digestive hormone ; GO:0046659 • %peptide hormone ; GO:0005180 %adrenocorticotropin ; GO:0017043 %glycopeptide hormone ; GO:0005181 %follicle-stimulating hormone ; GO:0016913 • % = IS A

10. as tree (joined by is a links): • hormone • digestive hormone peptide hormone • adrenocorticotropin glycopeptide hormone • follicle-stimulating hormone

11. Problem: There exist multiple databases • genomic • cellular • structural • phenotypic • … • and even for each specific type of information, e.g. DNA sequence data, there exist several databases of different scope and organisation

12. What is a gene? • GDB: a gene is a DNA fragment that can be transcribed and translated into a protein • Genbank: a gene is a DNA region of biological interest with a name and that carries a genetic trait or phenotype • (from Schulze-Kremer)

13. What is blood? • Unified Medical Language System (UMLS): • blood is a tissue • Systematized Nomenclature of Medicine(SNOMED): • blood is a fluid

14. Another Example: Statements of Accounts • Company Financial statements may be prepared under either the (US) GAAP or the (European) IASC standards • These allocate cost items to different categories depending on the laws of the countries involved.

15. Job: • to develop an algorithm for the automatic conversion of income statements and balance sheets between the two systems. • Not even this relatively simple problem has been satisfactorily resolved • … why not?

16. The World Wide Web • Vast amount of heterogeneous data sources • Needs: dramatically better support at the level of metadata • The ability to query and integrate across different conceptual systems: • The currently preferred answer is The Semantic Web, based on description logic • will not work: • How tag blood?

17. Application ontology • cannot solve the problems of database integration • There can be no mechanical solution to the problems of data fusion in a domain like medicine

18. Applications ontology: • … grew out of work in AI and in knowledge representation • Ontologies are applications running in real time

19. Applications ontology: • ontologies are inside the computer • thus subject to severe constraints on expressive power • (effectively the expressive power of description logic)

20. Applications ontology cannot solve the data-fusion problem • because of its roots in knowledge mining

21. different conceptual systems

22. need not interconnect at all

23. because of the limits of knowledge mining

24. we cannot make incompatible concept-systems interconnect just by looking at concepts, or knowledge – we need some tertium quid

25. Applications ontology • has its philosophical roots in Quine’s doctrine of ontological commitment and in the ‘internal metaphysics’ of Carnap/Putnam • Roughly, for an applications ontology the world and the semantic model are one and the same • What exists = what the system says exists

26. The Problem for the Quinean • If an ontology is the set of ontological commitments of a theory, how can we cope with questions pertaining to the relations between the objects to which different theories are committed?

27. theories, semantic models, need not interconnect at all

28. What is needed • in some sort of wider common framework which is sufficiently rich and nuanced to allow concept systems deriving from different sources to be hand-callibrated

29. What is needed • is not an applications ontology • but • a reference ontology • (something like old-fashioned metaphysics)

30. Reference Ontology • … grew out of logic and analytic metaphysics • An ontology is a theory of the relevant domain of entities • Ontology is outside the computer • seeks maximal expressiveness and adequacy to reality • willing to sacrifice computational tractability for the sake of representational adequacy

31. Belnap • “it is a good thing logicians were around before computer scientists; • “if computer scientists had got there first, then we wouldn’t have numbers • because arithmetic is undecidable”

32. It is a good thing • Aristotelian metaphysics was around before description logic, • because otherwise we would have only hierarchies of • concepts/universals/classes and no individual instances …

33. Reference Ontology • a theory of the tertium quid • – called • reality – • needed to hand-callibrate database/terminology systems

34. Methodology • Get ontology right first • (realism; descriptive adequacy; rather powerful logic); • solve tractability problems later

35. The Reference Ontology Community • IFOMIS (Leipzig) • Laboratory for Applied Ontology (Trento/Rome, Turin) • Foundational Ontology Project (Leeds) • Ontology Works (Baltimore) • Ontek Corporation (Buffalo/Leeds) • LandC (Belgium/Philadelphia) • (CYC?)

36. Domains of Current Work in Reference Ontology • IFOMIS Leipzig: Medicine • Laboratory for Applied Ontology • Trento/Rome: Ontology of Cognition/Language • Turin: Law • Foundational Ontology Project (Leeds): Space, Physics • Ontology Works (Baltimore): Genetics, Molecular Biology • Ontek Corporation (Buffalo/Leeds): Biological Systematics • LandC (Belgium/Philadelphia): Medical NLP • (? CYC : Everything ?)

37. Some Historical Background on Reference Ontology

38. Recall: • GDB: a gene is a DNA fragment that can be transcribed and translated into a protein • Genbank: a gene is a DNA region of biological interest with a name and that carries a genetic trait or phenotype • (from Schulze-Kremer)

39. Ontology • Note that terms like ‘fragment’, ‘region’, ‘name’, ‘carry’, ‘trait’, ‘type’ • … along with terms like ‘part’, ‘whole’, ‘function’, ‘substance’, ‘inhere’ … • are ontological terms in the sense of traditional (philosophical) ontology

40. Aristotle First ontologist

41. First ontology (from Porphyry’s Commentary on Aristotle’s Categories)

42. Linnaean Ontology

43. Formal Ontology • term coined by Edmund Husserl • = the theory of those ontological structures • such as part-whole, universal-particular • which apply to all domains whatsoever

44. Edmund Husserl

45. Husserl outlines a new methodof constituent ontology • to study a domain ontologically • is to establish the parts of the domain • and the interrelations between them • especially the dependence relations

46. Logical Investigations¸1900/01 • Aristotelian theory of universals and particulars • theory of part and whole • theory of ontological dependence • the theory of boundaries and fusion

47. Formal Ontology • contrasted with material or regional ontologies • (compare relation between pure and applied mathematics) • Husserl’s idea: • If we can build a good formal ontology, this should save time and effort in building reference ontologies for each successive domain

48. Basic Formal Ontology • BFO • The Vampire Slayer

49. Basic Formal Ontology • Aristotelian theory of universals and instances • theory of part and whole • theory of ontological dependence • theory of boundary, continuity and contact • theory of states, powers, qualities, roles (SPQR-entities) • theory of processes • theory of environments/niches/contexts and spatial and spatio-temporal regions

50. BFO • not just a system of categories • but a formal theory • with definitions, axioms, theorems • designed to provide the resources for reference ontologies for specific domains • the latter should be of sufficient richness that terminological incompatibilities can be resolves intelligently rather than by brute force