Semantic similarity measures applied to an ontology for human-like interaction

Authors:
Esperanza Albacete;Javier Calle;Elena Castro;Dolores Cuadra
Affiliations:
Computer Science Department, Carlos III University, Madrid, Spain;Computer Science Department, Carlos III University, Madrid, Spain;Computer Science Department, Carlos III University, Madrid, Spain;Computer Science Department, Carlos III University, Madrid, Spain
Venue:
Journal of Artificial Intelligence Research
Year:
2012

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Abstract

The focus of this paper is the calculation of similarity between two concepts from an ontology for a Human-Like Interaction system. In order to facilitate this calculation, a similarity function is proposed based on five dimensions (sort, compositional, essential, restrictive and descriptive) constituting the structure of ontological knowledge. The paper includes a proposal for computing a similarity function for each dimension of knowledge. Later on, the similarity values obtained are weighted and aggregated to obtain a global similarity measure. In order to calculate those weights associated to each dimension, four training methods have been proposed. The training methods differ in the element to fit: the user, concepts or pairs of concepts, and a hybrid approach. For evaluating the proposal, the knowledge base was fed from Word Net and extended by using a knowledge editing toolkit (Cognos). The evaluation of the proposal is carried out through the comparison of system responses with those given by human test subjects, both providing a measure of the soundness of the procedure and revealing ways in which the proposal may be improved.