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Representing the UMLS Semantic Network using OWL Vipul Kashyap 1 and Alex Borgida 2

Representing the UMLS Semantic Network using OWL Vipul Kashyap 1 and Alex Borgida 2 1 LHCNBC, National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894 2 Department of Computer Science, Rutgers University, New Brunswick, NJ 08903.

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Representing the UMLS Semantic Network using OWL Vipul Kashyap 1 and Alex Borgida 2

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  1. Representingthe UMLS Semantic Networkusing OWL Vipul Kashyap1 and Alex Borgida2 1 LHCNBC, National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894 2 Department of Computer Science, Rutgers University, New Brunswick, NJ 08903 Seminar Prinzipien des Ontological Engineering Leipzig, 15.01.2004 Kristin Lippoldt Email: kristin.lippoldt@imise.uni-leipzig.de

  2. Outline • The UMLS Semantic Network (SN) • Representation of SN using OWL • Multiple interpretations of „link“ • Evaluation of the interpretation variants • Methodology for choosing the „right“ representation variant (first steps)

  3. The UMLS Semantic Network • nodes = semantic types • links = semantic relationships • two high level is-a hierarchiesEntity, Event • is-a hierarchie of relationshipsphysically_related_to, spatially_related_to, temporally_related_to, functionally_related_to, conceptually_related_to functionally_related_to affects is-a manages is-a

  4. The UMLS Semantic Network (excerpt)

  5. OWL • Web Ontology Language • Based on DAML+OIL • Description of classes, properties (e.g. relations between classes (e.g. disjointness), cardinality (e.g. "exactly one")) • Sublanguages: • OWL Lite (lower formal complexity than OWL DL, only cardinality values of 0 or 1) • OWL DL (maximum expressiveness, computational completeness ) • OWL Full (maximum expressiveness, syntactic freedom of RDF with no computational guarantees)

  6. Description Logic - OWL Bacterium ODER Virus <owl:Class> <owl:unionOf rdf:parseType=“Collection”> <owl:Class rdf:about=“#Bacterium”/> <owl:Class rdf:about=“#Virus”/> </owl:unionOf> </owl:Class>

  7. Representation of SN using OWL • Semantic Types  OWL classes • Fungus  Organism • Virus  Organism • Semantic Relationships  OWL properties • part_of  physically_related_to • affects  functionally_related_to • Properties of Semantic Network Relationships • Asymmetric relationships • has_part ≡ part_of • Symmetric relationships • adjacent_to ≡ adjacent_to

  8. Semantics of a „link“ in the UMLS SN Bacteria Infection causes Two operators  and : • (causes) = { x  Bacteria  (y)(y  Infection  causes(x,y)) }DL notation: (causes) ≡ causes.T • (causes) = { y  Infection  (x)(x  Bacteria  causes(x,y)) }DL notation: (causes) ≡ causes.T

  9. Interpretation 1: / equals • axioms: causes.T ≡ Bacteria, causes.T≡ Infection • All Bacteria have to “cause” and all Infections have to“be-caused” (no others can participate in “causes”) b1 i1 b2 i2 b3 i3 b4

  10. Interpretation 2: / subsumed • axioms: causes.T  Bacteria, causes.T Infection • Not all bacteria need to “cause” not all infections have to “be-caused” (However no others can participate) i1 b2 i2 b3 i3 b4

  11. Interpretation 3: / subsumes • axioms: Bacteria  causes.T, Infection causes.T • All bacterias have to “cause” and all infections have to “be-caused”, but • A bacteria can cause a “non-infection” as well! • A “non-bacteria” can cause an infection as well! y1 i1 b2 i2 b3 i3 b4 x1

  12. Interpretation 4: All/Some • axiom: Bacteria  causes.Infection • All bacteria must “cause” some infection, but • A bacteria can cause a “non-infection” as well! • A “non-bacteria” can cause an infection as well! y1 i1 b2 i2 b3 i3 b4 x1

  13. Interpretation 5: All/Only • axiom: Bacteria  causes.Infection • All bacteria, if they “cause”, can cause only infections, but • Not all bacteria have to participate in the “causes” relationship • A non-bacteria can still cause an infection! y1 i1 b2 i2 b3 i3 b4

  14. Interpretation 6: All/Each • axiom: Bacteria  causes.Infection • Similar to a cross product, but • A bacteria can still cause a non-infection! i1 b2 i2 b3 i3 b4 x1

  15. Interpretation 7: Some/Some • axiom:  1 (Bacteria  causes.Infection) • There is at least one bacteria that “causes” at least one infection, but • A bacteria can still cause a non-infection! • A non-bacteria can still cause an infection! y1 i1 b2 i2 b3 i3 b4 x1

  16. Interpretation 8: Some/Each • axiom:  1 (Bacteria  causes.Infection) • There is at least one bacteria that “causes” all infections, but • A bacteria can still cause a non-infection! • A non-bacteria can still cause an infection! y1 i1 b2 i2 b3 i3 b4 x1

  17. Summary of Interpretations • equals: causes.T ≡ Bacteria, causes.T≡ Infection • subsumed: causes.T  Bacteria, causes.T Infection • subsumes: Bacteria  causes.T, Infection causes.T • all/some: Bacteria  causes.Infection • all/only: Bacteria  causes.Infection • all/each: Bacteria  causes.Infection • some/some:  1 (Bacteria  causes.Infection) • some/all:  1 (Bacteria  causes.Infection)

  18.  and  Inheritance  inheritance P(A,B) C  A P(C,B)  inheritance P(A,B) D  B P(A,D) Example: process_of(BiologicFunction,Organism) C = PhysiologicFunction D = Animal • equals: no support of inheritance , A ≡ C • subsumed: no support of inheritance A C process_of.T

  19.  and  Inheritance process_of.T process_of-.T • subsumes: supports both • all/some: supports  inheritance,but not  inheritance • all/only: supports  inheritance,but not  inheritance A B C D process_of.B process_of-.D A B C D process_of.B A C

  20.  and  Inheritance process_of. D • all/each: supports both • some/some: no support of inheritance • some/all: doesn’t supports  inheritance, but  inheritance process_of. B A C

  21. Blocking of Inheritance Example: Process_of(BiologicFunction,Organism) Process_of(MentalProcess,Plant) Modifying axioms: subsumes: P(A,B) C1 A and D1 B A  C1 (P) and B  D1 (P)

  22. Ergebnis

  23. Methodologie für die Kodierung von Wissen im Semantic Web • Wahl der Kodierung • Unterstützung von Inferenz • Unterstützung der intendierten Anwendung • Nachvollziehbares Domänenmodell • Repräsentation in der Ontologiesprache

  24. Unterstützung von Inferenzen • Welche Kodierung unterstützt Inferenz? • All/each und subsumes • Unterstützt die Kodierung nicht-intendierte Inferenzen? • Some/some unterstützt Aufwärts-Vererbung von Links • Kann etwas aus der Abwesenheit eines Links geschlussfolgert werden? • A  P. B verbietet nicht, dass A in Relation zu B steht

  25. Unterstützung der intendierten Anwendung • Ist es wichtig Inkonsistenzen zu erkennen? • Was sind Inkonsistenzen? • Wird die Kodierung diese Inkonsistenzen erkennen?

  26. Nachvollziehbarkeit des Domänenmodells • Konzepte sind Kollektionen von Instanzen • Causes(Bacteria,Infection) • Was ist die intuitive Kodierung? • All/some and all/only wird von medizinischen Ontologien genutzt • All/each und some/some wurden abgelehnt • Gibt es alternative Interpretationen? • Aber: all/each erfüllt alle UMLS SN Anforderungen

  27. Repräsentation in der Ontologiesprache • Grenzen von OWL • Negation und Disjunktion von Rollen • Kardinalität von Konzepten • Kann man weniger „teure“ Konstrukte verwenden? • Ressourcen fließen in die Komplexität der DL Operatoren

  28. Conclusions and Future Work • Experiences in representing a real world “ontology”, the UMLS Semantic Network • Has been used very successfully • Requirements: / inheritance, inheritance blocking, polymorphic relationships • Presented multiple interpretations and encodings and evaluated their support for the UMLS Semantic Network requirements • Ontology developers and encoders on the Semantic Web might encounter similar requirements and possible encodings • Identified criteria for choosing between the various encodings • First steps towards a methodology which might be useful to ontology developers • Ongoing and Future Work • Semantic Vocabulary Interoperation Project • http://cgsb2.nlm.nih.gov/~kashyap/projects/SVIP • Use of OWL, RDF for improvement in Medical Information Retrieval

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