Ontology Mapping - Out Of The Babel Tower

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Keynote at the AI in Medicine Conference (AIME 2005), giving an overview of the work in Ontology Mapping to people in Medical Informatics (which includes explaining the what and why of ontologies in general).

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Ontology Mapping - Out Of The Babel Tower

  1. 1. Ontology mapping: a way out of the medical tower of Babel? Frank van Harmelen Vrije Universiteit Amsterdam The Netherlands Antilles
  2. 2. Before we start…  a talk on ontology mappings is difficult talk to give:  no concensus in the field • on merits of the different approaches • on classifying the different approaches  no one can speak with authority on the solution  this is a personal view, with a sell-by date  other speakers will entirely disagree (or disapprove)
  3. 3. Good overviews of the topic  Knowledge Web D2.2.3: “State of the art on ontology alignment”  Ontology Mapping Survey talk by Siyamed Seyhmus SINIR  ESWC'05 Tutorial on Schema and Ontology Matching by Pavel Shvaiko Jerome Euzenat  KER 2003 paper Kalfoglou & Schorlemmer  These are all different & incompatible…
  4. 4. Ontology mapping: a way out of the medical tower of Babel?
  5. 5. The Medical tower of Babel  Mesh • Medical Subject Headings, National Library of Medicine • 22.000 descriptions  EMTREE • Commercial Elsevier, Drugs and diseases • 45.000 terms, 190.000 synonyms  UMLS • Integrates 100 different vocabularies  SNOMED • 200.000 concepts, College of American Pathologists  Gene Ontology • 15.000 terms in molecular biology  NCI Cancer Ontology: • 17,000 classes (about 1M definitions),
  6. 6. Ontology mapping: a way out of the medical tower of Babel?
  7. 7. What are ontologies & what are they used for world concept language Agree on a no shared understanding conceptualization Conceptual and terminological confusion Make it explicit in some language. Actors: both humans and machines
  8. 8. Ontologies come in very different kinds  From lightweight to heavyweight: • Yahoo topic hierarchy • Open directory (400.000 general categories) • Cyc, 300.000 axioms  From very specific to very general • METAR code (weather conditions at air terminals) • SNOMED (medical concepts) • Cyc (common sense knowledge)
  9. 9. What’s inside an ontology?  terms + specialisation hierarchy  classes + class-hierarchy  instances  slots/values  inheritance (multiple? defaults?)  restrictions on slots (type, cardinality)  properties of slots (symm., trans., …)  relations between classes (disjoint, covers)  reasoning tasks: classification, subsumption Increasing semantic “weight”
  10. 10. In short (for the duration of this talk)  Ontologies are not definitive descriptions of what exists in the world (= philosphy)  Ontologies are models of the world constructed to facilitate communication  Yes, ontologies exist (because we build them)
  11. 11. Ontology mapping: a way out of the medical tower of Babel?
  12. 12.  Ontology mapping is old & inevitable  Ontology mapping is old • db schema integration • federated databases  Ontology mapping is inevitable • ontology language is standardised, • don't even try to standardise contents
  13. 13.  Ontology mapping is important  database integration, heterogeneous database retrieval (traditional)  catalog matching (e-commerce)  agent communication (theory only)  web service integration (urgent)  P2P information sharing (emerging)  personalisation (emerging)
  14. 14.  Ontology mapping is now urgent  Ontology mapping has acquired new urgency • physical and syntactic integration is ± solved, (open world, web) • automated mappings are now required (P2P) • shift from off-line to run-time matching  Ontology mapping has new opportunities • larger volumes of data • richer schemas (relational vs. ontology) • applications where partial mappings work
  15. 15. Different aspects of ontology mapping  how to discover a mapping  how to represent a mapping • subset/equal/disjoint/overlap/ is-somehow-related-to • logical/equational/category-theoretical  atomic/complex arguments,  confidence measure  how to use it We only talk about “how to discover”
  16. 16. Many experimental systems: (non-exhaustive!)  Prompt (Stanford SMI)  Coma (ULeipzig)  Anchor-Prompt (Stanford SMI)  Buster (UBremen)  Chimerae (Stanford KSL)  MULTIKAT (INRIA S.A.)  Rondo (Stanford U./ULeipzig)  ASCO (INRIA S.A.)  MoA (ETRI)  OLA (INRIA R.A.)  Cupid (Microsoft research)  Dogma's Methodology  Glue (Uof Washington)  ArtGen (Stanford U.)  FCA-merge (UKarlsruhe)  Alimo (ITI-CERTH)  IF-Map  Bibster (UKarlruhe)  Artemis (UMilano)  QOM (UKarlsruhe)  T-tree (INRIA Rhone-Alpes)  KILT (INRIA LORRAINE)  S-MATCH (UTrento)
  17. 17. Different approaches to ontology matching  Linguistics & structure  Shared vocabulary  Instance-based matching  Shared background knowledge
  18. 18. Linguistic & structural mappings  normalisation (case,blanks,digits,diacritics)  lemmatization, N-grams, edit-distance, Hamming distance,  distance = fraction of common parents  elements are similar if their parents/children/siblings are similar decreasing order of boredom
  19. 19. Different approaches to ontology matching  Linguistics & structure  Shared vocabulary  Instance-based matching  Shared background knowledge
  20. 20. Matching through shared vocabulary Q Low(Q) Q Up(Q)  Low(Q) µ Q µ  Up(Q)
  21. 21. Matching through shared vocabulary  Used in mapping geospatial databases from German land-registration authorities (small)  Used in mapping bio-medical and genetic thesauri (large)
  22. 22. Different approaches to ontology matching  Linguistics & structure  Shared vocabulary  Instance-based matching  Shared background knowledge
  23. 23. Matching through shared instances
  24. 24. Matching through shared instances  Used by Ichise et al (IJCAI’03) to succesfully map parts of Yahoo to parts of Google  Yahoo = 8402 classes, 45.000 instances  Google = 8343 classes, 82.000 instances  Only 6000 shared instances  70% - 80% accuracy obtained (!)  Conclusions from authors: • semantics is needed to improve on this ceiling
  25. 25. Different approaches to ontology matching  Linguistics & structure  Shared vocabulary  Instance-based matching  Shared background knowledge
  26. 26. Matching using shared background knowledge shared background knowledge ontology 1 ontology 2
  27. 27. Ontology mapping using background knowledge Case study 1 PHILIPS Work with Zharko Aleksovski @ Philips • Michel Klein @ VU KIK @ AMC
  28. 28. Overview of test data Two terminologies from intensive care domain  OLVG list • List of reasons for ICU admission  AMC list • List of reasons for ICU admission  DICE hierarchy • Additional hierarchical knowledge describing the reasons for ICU admission
  29. 29. OLVG list  developed by clinician  3000 reasons for ICU admission  1390 used in first 24 hours of stay • 3600 patients since 2000  based on ICD9 + additional material  List of problems for patient admission  Each reason for admission is described with one label • Labels consist of 1.8 words on average • redundancy because of spelling mistakes • implicit hierarchy (e.g. many fractures)
  30. 30. AMC list  List of 1460 problems for ICU admission  Each problem is described using 5 aspects from the DICE terminology:  2500 concepts (5000 terms), 4500 links • Abnormality (size: 85) • Action taken (size: 55) • Body system (size: 13) • Location (size: 1512) • Cause (size: 255)  expressed in OWL  allows for subsumption & part-of reasoning
  31. 31. Why mapping AMC list $ OLVG list?  allow easy entering of OLVG data  re-use of data in • epidemiology • quality of care assessment • data-mining (patient prognosis)
  32. 32. Linguistic mapping:  Compare each pair of concepts  Use labels and synonyms of concepts  Heuristic method to discover equivalence and subclass relations Long brain tumor More specific Long tumor than  First round • compare with complete DICE • 313 suggested matches, around 70 % correct  Second round: • only compare with “reasons for admission” subtree • 209 suggested matches, around 90 % correct  High precision, low recall (“the easy cases”)
  33. 33. Using background knowledge  Use properties of concepts  Use other ontologies to discover relation between properties ? …. …. …. …. …. ….
  34. 34. Semantic match DICE aspect Lexical match taxonomies Given ? Abnormality taxonomy ? Action taxonomy ? Body system taxonomy ? Location taxonomy ? Cause taxonomy Implicit OLVG matching: DICE problem list property problem list match
  35. 35. Semantic match Taxonomy of body parts Blood vessel is more general is more general Vein Artery is more general Aorta Lexical match: Lexical match: has location Reasoning: has location implies Aorta thoracalis dissection Dissection of artery Location match: has more general location
  36. 36. Example: “Heroin intoxication” – “drugs overdose” Cause taxonomy Drugs is more general Heroine Lexical match: Lexical Cause match: match: cause has more specific cause cause Heroin intoxication Drugs overdosis Abnormality match: has more general Lexical abnormality Lexical match: match: abnormality abnormality Abnormality taxonomy Intoxicatie is more general Overdosis
  37. 37. Example results • OLVG: Acute respiratory failure abnormality DICE: Asthma cardiale • OLVG: Aspergillus fumigatus cause DICE: Aspergilloom • OLVG: duodenum perforation abnormality, DICE: Gut perforation cause • OLVG: HIV cause DICE: AIDS • OLVG: Aorta thoracalis dissectie type B location, DICE: Dissection of artery abnormality
  38. 38. Extension: approximate matching  Terms are not precisely defined  Terms are not precisely used Exact reasoning will not be useful A B A½B?
  39. 39. Approximate matching  Translate every class-name into a propositional formula (both DNF and CNF versions) A ⊆ B = (∪Ai ⊆ ∩Bk) = ∀i,k (Ai ⊆ Bk)  ignore increasing number. of (i,k)-subsumption pairs  varies from classical to trivial
  40. 40. Results (obtained on different domain) 600000 500000 400000 B subClass of A 300000 A subClass of B equivalences 200000 100000 0 0 3 4 5 6 8 9 1 2 7 0 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.
  41. 41. Ontology mapping using background knowledge Case study 2 Work with Heiner Stuckenschmidt @ VU
  42. 42. Case Study:  Map GALEN & Tambis, using UMLS as background knowledge  Select three topics with sufficient overlap • Substances • Structures • Processes  Define some partial & ad-hoc manual mappings between individual concepts  Represent mappings in C-OWL  Use semantics of C-OWL to verify and complete mappings
  43. 43. Case Study: verification & verification & derivation UMLS derivation (medical terminology) lexical mapping lexical mapping GALEN Tambis (medical ontology) derived mapping (genetic ontology)
  44. 44. Ad hoc mappings: Substances UMLS GALEN Notice: mappings high and low in the hierarchy, few in the middle
  45. 45. Ad hoc mappings: Substances UMLS Tambis Notice different grainsize: UMLS course, Tambis fine
  46. 46. Verification of mappings = UMLS:Chemicals UMLS:Chemicals_ Tambis:Chemical viewed_structurally Tambis:enzyme ⊥? UMLS:Chemicals_ viewed_functionally UMLS:enzyme =
  47. 47. Deriving new mappings UMLS:substance UMLS:Phenomenon_ or_process UMLS:Chemicals ⊥ Galen: ChemicalSubstance UMLS:OrganicChemical ⊇ = ⊆
  48. 48. Ontology mapping: a way out of the medical tower of Babel?
  49. 49. “Conclusions”  Ontology mapping is (still) hard & open  Many different approaches will be required: • linguistic, • structural • statistical • semantic • …  Currently no roadmap theory on what's good for which problems
  50. 50. Challenges  roadmap theory  run-time matching  “good-enough” matches  large scale evaluation methodology  hybrid matchers (needs roadmap theory)
  51. 51. Ontology mapping: a way out of the medical tower of Babel? ?

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