A MATHEMATICAL MODEL FOR SEMANTIC SIMILARITY MEASURES

Dinh Que Tran, Nguyen Manh Hung

Abstract


Semantic similarity between words, concepts or sets of concepts has
been a fundamental theme and widely studied in various areas including
natural language processing, document semantic comparison, artificial
intelligence, semantic web, semantic web service, and semantic search
engines. Several similarity measures have been proposed but they are
usually tied to special application domains or information representation
of various application domains.
In this paper, we present a mathematical model for distance-based
semantic similarity estimation in domains that are represented with ontology
- an explicit specification of conceptualization of such domains.
Based on this model, we construct algorithms to calculate the semantic
similarity between two concepts and one between two sets of concepts.
The significance of the proposed mathematical model is that it offers
a generalization that enables to maintain flexibility and thus supports
various computational measures.

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