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

Ontological Distance Measure for Information Visualisation on Conceptual Maps - S. Ranwez 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

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)

Complementarity of the two

approaches

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

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)

d(Veterinarians, Nurses) < d(Trustees, Nurses)

d(Nurses, Health Personnel) < d(Veterinarians, Health Personnel)

(encompasses few more concepts)

T

 Health Personnel (20)  Administrative Personnel (4)

Persons (44)

Occupational Groups (12)

Nurses (6)

Trustees (0)

Veterinarians (0)

…

…

…

Dentists (1)

Physician Executives (0)

[MeSH]

Ontological Distance Measure for Information Visualisation on Conceptual Maps - S. Ranwez 6

From ontology to semantic distance

Intuitive approach on the is-a relation

However multiple inheritance (points of view) must be taken into account

 Health Personnel (20)

 Nurses Administrators (0)

 Administrative Personnel (4)

Persons (44)

Occupational Groups (12)

Nurses (6)

Trustees (0)

Veterinarians (0)

…

…

…

Dentists (1)

T

Physician Executives (0)

Ontological Distance Measure for Information Visualisation on Conceptual Maps - S. Ranwez 7

From ontology to semantic distance

ancExc(a,b)desc( ancExc(a,b) )desc( ancExc(a,b) ) desc(a) desc(b)

dISA(a, b) = 11

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) |

T

C0

C1 C2 C3

aC4 C5 C8C7C6 b

C11C9 C10

Definition

Ontological Distance Measure for Information Visualisation on Conceptual Maps - S. Ranwez 8

From ontology to semantic distance

dISA(a, b) = | desc( ancExc(a, b) ) desc(a) desc(b) - desc(a) desc(b) |

dISA(Trust., Nur.) = | {Health P., Dentists, …, Nur., Nur. adm., Admin P., …, Trust.} | = 59dISA(Trust., Nur.) = | desc(Health P., Admin P.) {Nur., …, Nur. adm.} {Trust.} - |

Example

dISA(Trust., Nur.) = | desc( ancExc(Trust., Nur.) desc(Nur.) desc(Trust.) - desc(Nur.) desc(Trust.) |

dISA(Nur. adm., Phys. Exec.) = 8 dISA(Trust., Phys. Exec.) = 58

…

Physician Executives (0)

 Health Personnel (20)

 Nurses Administrators (0)

 Administrative Personnel (4)

Persons (44)

Occupational Groups (12)

Nurses (6)

Trustees (0)

Veterinarians (0)…

…

Dentists (1)

…

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

Resulting visualisation

 Health Personnel (20)

 Nurses Administrators (0)

 Administrative Personnel (4)

Persons (44)

Occupational Groups (12)

Nurses (6)

Trustees (0)

Veterinarians (0)

…

…

…

Dentists (1)

…

dISA(Trust., Nur.) = 59dISA(Nur. adm., Phys. Exec.) = 8dISA(Trust., Phys. Exec.) = 58dISA(Nur., Phys. Exec.) = 13

Ontological Distance Measure for Information Visualisation on Conceptual Maps - S. Ranwez 12

Resulting visualisation

Example from the MeSH

Nervous System Diseases

Central Nervous System Diseases

Brain Diseases

Headache Disorder, Primary

Migraine = Migraine Disorder

Sign and Symptoms

Headache

Neurologic Manifestations

Migraine Disorder with Aura

Migraine Disorder without Aura

Headache DisorderPain

…

Pathological Conditions, Signs and Symptoms

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

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 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 Measures for Information Visualisation on Conceptual Maps

Sylvie.Ranwez@ema.frhttp://www.lgi2p.ema.fr/~ranwezs

Vincent.Ranwez@isem.univ-montp2.frhttp://ranwez.free.fr/

Jean.Villerd@ema.frhttp://www.lgi2p.ema.fr/~villerd

Michel.Crampes@ema.frhttp://www.ema.fr/~mcrampes