Ontological Distance Measures for Information Visualisation on Conceptual

Overview § Semantic distances: state-of-the-Art § From ontology to semantic distance • • Intuitive

Semantic distances: state-of-the-Art Estimating similarity between concepts Methods based on the concept hierarchy §

From ontology to semantic distance § Intuitive approach on the is-a relation Two concepts

From ontology to semantic distance § Intuitive approach on the is-a relation However multiple

From ontology to semantic distance § Definition T C 0 C 1 C 4

From ontology to semantic distance d. ISA(a, b) = | desc( anc. Exc(a, b)

Overview § Semantic distances: state-of-the-Art § From ontology to semantic distance • Intuitive approach

Resulting visualisation d. ISA(Trust. , Nur. ) = 59 d. ISA(Nur. adm. , Phys.

Resulting visualisation Nervous System Diseases Example from the Me. SH Central Nervous System Diseases

Discussion and perspectives Towards a semantic distance § § § Combine the ISA distance

Conclusion § Proposition of a distance using ISA relations, that respects the distance properties

Ontological Distance Measures for Information Visualisation on Conceptual Maps Sylvie. Ranwez@ema. fr http: //www.

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Ontological Distance Measures for Information Visualisation on Conceptual Maps Sylvie Ranwez Jean Villerd Michel Crampes LGI 2 P Research Centre – EMA, Nîmes Vincent Ranwez ISEM – Montpellier University

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 2

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 § § Complementarity of the two approaches 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 3

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) T [Me. SH] Persons (44) Occupational Groups (12) … Health Personnel (20) Dentists (1) … Veterinarians (0) Administrative Personnel (4) Nurses (6) d(Veterinarians, Nurses) < d(Trustees, Nurses) Physician Executives (0) … Trustees (0) d(Nurses, Health Personnel) < d(Veterinarians, Health Personnel) Ontological Distance Measure for Information Visualisation on Conceptual Maps - S. Ranwez 5

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

From ontology to semantic distance § Definition T C 0 C 1 C 4 a C 9 C 2 C 5 C 3 C 6 C 10 C 7 b C 8 C 11 )) desc(a) anc. Exc(a, b) desc( anc. Exc(a, b) ) desc(b) - desc(a) desc(b) d. ISA(a, b) = |desc( anc. Exc(a, b) desc(a) desc(b) - desc(a) desc(b) | d. ISA(a, b) = 11 Ontological Distance Measure for Information Visualisation on Conceptual Maps - S. Ranwez 7

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

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

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 10

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

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

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 1. Visualisation of ontologies by projection and identification of clusters 2. Use of traditional clustering methods (hierarchical clustering, K-means…) 3. 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 13

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 14

Ontological Distance Measures for Information Visualisation on Conceptual Maps Sylvie. Ranwez@ema. fr http: //www. lgi 2 p. ema. fr/~ranwezs Vincent. Ranwez@isem. univ-montp 2. fr http: //ranwez. free. fr/ Jean. Villerd@ema. fr http: //www. lgi 2 p. ema. fr/~villerd Michel. Crampes@ema. fr http: //www. ema. fr/~mcrampes