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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
[email protected]://www.lgi2p.ema.fr/~ranwezs
[email protected]://ranwez.free.fr/
[email protected]://www.lgi2p.ema.fr/~villerd
[email protected]://www.ema.fr/~mcrampes