Ontological Distance Measures for Information Visualisation on Conceptual Maps

1. Ontological Distance Measures for Information Visualisation on Conceptual Maps Sylvie Ranwez Vincent Ranwez Jean Villerd Michel Crampes LGI2P Research Centre – EMA, Nîmes ISEM – Montpellier University

2. Overview • Semantic distances: state-of-the-Art • From ontology to semantic distance • Intuitive approach • Formal definition • Example • Distance properties • Resulting visualisation • Discussion and perspectives • Conclusion Ontological Distance Measure for Information Visualisation on Conceptual Maps - S. Ranwez

3. Complementarity of the two approaches Semantic distances: state-of-the-Art Estimating similarity between concepts • Methods based on the concept hierarchy • d(a, b): the length of the shortest path between a and b[Sowa] • sim(a, b): function of common subsumers[Resnik] Considers only one point of view on the concept Supposes homogeneity of branches’ semantic Does not respect distances properties • Methods based on vectors calculus • Vectors of terms to describe a document • Vectors of concepts to describe a given concept • Ensemblist methods (Dice or Jaccard) • Geometric methods (cosines), Euclidian measure, distributional, etc. Vectors are not always available Lack of precision due to the vectorisation (synonyms) Ontological Distance Measure for Information Visualisation on Conceptual Maps - S. Ranwez

4. Overview • Semantic distances: state-of-the-Art • From ontology to semantic distance • Intuitive approach • Formal definition • Example • Distance properties • Resulting visualisation • Discussion and perspectives • Conclusion Ontological Distance Measure for Information Visualisation on Conceptual Maps - S. Ranwez

5. T [MeSH] Persons (44) Occupational Groups (12) … Administrative Personnel (4)  Health Personnel (20) Dentists (1) … Veterinarians (0) Nurses (6) … Trustees (0) Physician Executives (0) From ontology to semantic distance • Intuitive approach on the is-a relation • Two concepts are close if there is a concept that sumbsumes both of them and if this concept is slightly more general (encompasses few more concepts) (encompasses few more concepts) d(Veterinarians, Nurses) < d(Trustees, Nurses) d(Nurses, Health Personnel) < d(Veterinarians, Health Personnel) Ontological Distance Measure for Information Visualisation on Conceptual Maps - S. Ranwez

6. T Persons (44) Occupational Groups (12) …  Administrative Personnel (4)  Health Personnel (20) Dentists (1) … Veterinarians (0) Nurses (6) …  Nurses Administrators (0) Physician Executives (0) Trustees (0) From ontology to semantic distance • Intuitive approach on the is-a relation However multiple inheritance (points of view) must be taken into account Ontological Distance Measure for Information Visualisation on Conceptual Maps - S. Ranwez

7. T C0 C1 C2 C3 C4 a C5 C6 C7 b C8 C9 C10 C11 desc( ancExc(a,b) )  desc(a)  desc(b)- desc(a)  desc(b) dISA(a, b) = | desc( ancExc(a, b) )  desc(a)  desc(b) - desc(a)  desc(b) | From ontology to semantic distance • Definition • ancExc(a,b) desc( ancExc(a,b) )  desc(a)  desc(b) desc( ancExc(a,b) ) dISA(a, b) = 11 Ontological Distance Measure for Information Visualisation on Conceptual Maps - S. Ranwez

8. … Persons (44) Occupational Groups (12) …  Administrative Personnel (4)  Health Personnel (20) Dentists (1) … Veterinarians (0) Nurses (6) … Physician Executives (0)  Nurses Administrators (0) Trustees (0) From ontology to semantic distance • dISA(a, b) = | desc( ancExc(a, b) )  desc(a)  desc(b) - desc(a)  desc(b) | • Example dISA(Trust., Nur.) = | desc( ancExc(Trust., Nur.)  desc(Nur.)  desc(Trust.) - desc(Nur.)  desc(Trust.) | dISA(Trust., Nur.) = | desc(Health P., Admin P.)  {Nur., …, Nur. adm.}  {Trust.} - | dISA(Trust., Nur.) = | {Health P., Dentists, …, Nur., Nur. adm., Admin P., …, Trust.}| = 59 dISA(Nur. adm., Phys. Exec.) = 8 dISA(Trust., Phys. Exec.) = 58 dISA(Nur., Phys. Exec.) = 13 Ontological Distance Measure for Information Visualisation on Conceptual Maps - S. Ranwez

9. From ontology to semantic distance • dISA(a, b) = | desc( ancExc(a, b) )  desc(a)  desc(b) - desc(a)  desc(b) | • Respects the three properties of a distance • Positiveness :  a, b dISA(a, b)0 and dISA(a, b) = 0  a = b • Symmetry :  a, b dISA(a, b) = dISA(b, a) • Triangle inequality :  a, b, c dISA(a, c) + dISA(c, b) dISA(a, b) • Extension • Intuitive distance in a tree-like hierarchy when a subsumes b dISA(a, b) = | desc(a) – desc(b) | Ontological Distance Measure for Information Visualisation on Conceptual Maps - S. Ranwez

10. Overview • Semantic distances: state-of-the-Art • From ontology to semantic distance • Intuitive approach • Formal definition • Example • Resulting visualisation • Discussion and perspectives • Conclusion Ontological Distance Measure for Information Visualisation on Conceptual Maps - S. Ranwez

11. … Persons (44) Occupational Groups (12) …  Health Personnel (20)  Administrative Personnel (4) … Dentists (1) Veterinarians (0) Nurses (6) …  Nurses Administrators (0) Trustees (0) Resulting visualisation dISA(Trust., Nur.) = 59 dISA(Nur. adm., Phys. Exec.) = 8 dISA(Trust., Phys. Exec.) = 58 dISA(Nur., Phys. Exec.) = 13 Ontological Distance Measure for Information Visualisation on Conceptual Maps - S. Ranwez

12. Nervous System Diseases Neurologic Manifestations Pathological Conditions, Signs and Symptoms Central Nervous System Diseases Brain Diseases Sign and Symptoms Headache Disorder Pain Headache Disorder, Primary … Headache Migraine = Migraine Disorder Migraine Disorder with Aura Migraine Disorder without Aura Resulting visualisation Example from the MeSH Ontological Distance Measure for Information Visualisation on Conceptual Maps - S. Ranwez

13. Discussion and perspectives • Towards a semantic distance • Combine the ISA distance with other distance measures taking into account other kinds of relations • Combine with approaches using vector calculus • Combine the ISA distance with the level of detail of the concepts • Validation and extension of the visualisation • Visualisation of ontologies by projection and identification of clusters • Use of traditional clustering methods (hierarchical clustering, K-means…) • Comparisons and validation of our approach Enforce the use in industrial context • Validation of existing ontologies • Support during the conception of new ontologies • Support while navigating or searching for information Ontological Distance Measure for Information Visualisation on Conceptual Maps - S. Ranwez

14. Conclusion • Proposition of a distance using ISA relations, that respects the distance properties • Positiveness • Symmetry • Triangle inequality • Projection of ontologies: a new way of visualising ontologies • Towards conceptual maps • Support in ontologies building and validating • Application • Ontology design • Navigation support • Information retrieval Ontological Distance Measure for Information Visualisation on Conceptual Maps - S. Ranwez

15. Ontological Distance Measures for Information Visualisation on Conceptual Maps Sylvie.Ranwez@ema.fr http://www.lgi2p.ema.fr/~ranwezs Vincent.Ranwez@isem.univ-montp2.fr http://ranwez.free.fr/ Jean.Villerd@ema.fr http://www.lgi2p.ema.fr/~villerd Michel.Crampes@ema.fr http://www.ema.fr/~mcrampes