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Representing the UMLS Semantic Network using OWL Vipul Kashyap 1 and Alex Borgida 2 1 LHCNBC, National Library of Medicine, 8600 Rockville Pike, Bethesda,

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Presentation on theme: "Representing the UMLS Semantic Network using OWL Vipul Kashyap 1 and Alex Borgida 2 1 LHCNBC, National Library of Medicine, 8600 Rockville Pike, Bethesda,"— Presentation transcript:

1 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 Department of Computer Science, Rutgers University, New Brunswick, NJ Seminar Prinzipien des Ontological Engineering Leipzig, Kristin Lippoldt

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 hierarchies Entity, Event is-a hierarchie of relationships physically_related_to, spatially_related_to, temporally_related_to, functionally_related_to, conceptually_related_to functionally_related_to affects 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

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 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 BacteriaInfection causes

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) b1b1 i1i1 b2b2 i2i2 b3b3 i3i3 b4b4

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) i1i1 b2b2 i2i2 b3b3 i3i3 b4b4

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! i1i1 b2b2 i2i2 b3b3 i3i3 b4b4 x1x1 y1y1

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! i1i1 b2b2 i2i2 b3b3 i3i3 b4b4 x1x1 y1y1

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! i1i1 b2b2 i2i2 b3b3 i3i3 b4b4 y1y1

14 Interpretation 6: All/Each axiom: Bacteria causes. Infection Similar to a cross product, but –A bacteria can still cause a non-infection! i1i1 b2b2 i2i2 b3b3 i3i3 b4b4 x1x1

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! i1i1 b2b2 i2i2 b3b3 i3i3 b4b4 x1x1 y1y1

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! i1i1 b2b2 i2i2 b3b3 i3i3 b4b4 x1x1 y1y1

17 Summary of Interpretations 1)equals: causes.T Bacteria, causes.T Infection 2)subsumed: causes.T Bacteria, causes.T Infection 3)subsumes:Bacteria causes.T, Infection causes.T 4)all/some:Bacteria causes.Infection 5)all/only:Bacteria causes.Infection 6)all/each:Bacteria causes. Infection 7)some/some: 1 (Bacteria causes.Infection) 8)some/all: 1 (Bacteria causes. Infection)

18 and Inheritance inheritanceP(A,B) C A P(C,B) inheritanceP(A,B) D B P(A,D) Example:process_of(BiologicFunction,Organism) C = PhysiologicFunction D = Animal 1)equals:no support of inheritance, A C 2)subsumed:no support of inheritance A C process_of.T

19 and Inheritance 3)subsumes: supports both 4)all/some: supports inheritance, but not inheritance 5)all/only: supports inheritance, but not inheritance A C process_of.T B D process_of -.T A C process_of.B B D process_of -.D process_of.B A C

20 and Inheritance 6)all/each: supports both 7)some/some: no support of inheritance 8)some/all: doesnt supports inheritance, but inheritance A C process_of. B process_of. D

21 Blocking of Inheritance Example: Process_of(BiologicFunction,Organism) Process_of(MentalProcess,Plant) Modifying axioms: subsumes:P(A,B) C 1 A and D 1 B A C 1 (P) and B D 1 (P)

22 Ergebnis InterpretationEncoding / Inheritance Inheritance BlockingPolymorphic Relations / equals (P) A (P) B No/NoN/ANo / subsumed (P) A (P) B No/NoN/AMissed model / subsumes A (P) B (P) Yes/Yes Exceptions + compensation Unintended model all / some A P.B Yes/NoException in axiomok all / only A P.B Yes/NoException in axiomModification some / some 1(A P.B) No/NoN/Aok some / all 1(A P. B) No/YesException in axiomok all / each A P. B Yes/Yes Exceptions + compensation ok

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 –Use of OWL, RDF for improvement in Medical Information Retrieval


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