Ontology Mapping - Out Of The Babel Tower

Ontology mapping: a way out of the medical tower of Babel? Frank van Harmelen Vrije Universiteit Amsterdam The Netherlands Antilles

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)

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…

Ontology mapping: a way out of the medical tower of Babel?

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),

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

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)

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”

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)

 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

 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)

 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

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”

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)

Different approaches to ontology matching  Linguistics & structure  Shared vocabulary  Instance-based matching  Shared background knowledge

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

Matching through shared vocabulary Q Low(Q) Q Up(Q)  Low(Q) µ Q µ  Up(Q)

Matching through shared vocabulary  Used in mapping geospatial databases from German land-registration authorities (small)  Used in mapping bio-medical and genetic thesauri (large)

Matching through shared instances

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

Matching using shared background knowledge shared background knowledge ontology 1 ontology 2

Ontology mapping using background knowledge Case study 1 PHILIPS Work with Zharko Aleksovski @ Philips • Michel Klein @ VU KIK @ AMC

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

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)

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

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)

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”)

Using background knowledge  Use properties of concepts  Use other ontologies to discover relation between properties ? …. …. …. …. …. ….

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

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

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

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

Extension: approximate matching  Terms are not precisely defined  Terms are not precisely used Exact reasoning will not be useful A B A½B?

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

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.

Ontology mapping using background knowledge Case study 2 Work with Heiner Stuckenschmidt @ VU

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

Case Study: verification & verification & derivation UMLS derivation (medical terminology) lexical mapping lexical mapping GALEN Tambis (medical ontology) derived mapping (genetic ontology)

Ad hoc mappings: Substances UMLS GALEN Notice: mappings high and low in the hierarchy, few in the middle

Ad hoc mappings: Substances UMLS Tambis Notice different grainsize: UMLS course, Tambis fine

Verification of mappings = UMLS:Chemicals UMLS:Chemicals_ Tambis:Chemical viewed_structurally Tambis:enzyme ⊥? UMLS:Chemicals_ viewed_functionally UMLS:enzyme =

Deriving new mappings UMLS:substance UMLS:Phenomenon_ or_process UMLS:Chemicals ⊥ Galen: ChemicalSubstance UMLS:OrganicChemical ⊇ = ⊆

“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

Challenges  roadmap theory  run-time matching  “good-enough” matches  large scale evaluation methodology  hybrid matchers (needs roadmap theory)

Ontology mapping: a way out of the medical tower of Babel? ?

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Ontology Mapping - Out Of The Babel Tower

by Frank Van Harmelen on Jul 05, 2009

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Ontology Mapping - Out Of The Babel Tower — Presentation Transcript