RT Journal Article
T1 Semantic Similarity Measures Applied to an Ontology for Human-Like Interaction
A1 Albacete García, Esperanza
A1 Calle Gómez, Francisco Javier
A1 Castro Galán, Elena
A1 Cuadra Fernández, María Dolores
AB 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 WordNet 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.
PB Artificial Intelligence Foundation
SN 1076-9757
YR 2012
FD 2012-07
LK https://hdl.handle.net/10016/20226
UL https://hdl.handle.net/10016/20226
LA eng
NO The development of this approach and its construction as part of the LaBDA-Interactor Human-Like Interaction System, part of the research projects SemAnts (TSI-020110-2009-419) and THUBAN (TIN2008-02711) and CADOOH (TSI-020302-2011-21), is supported by the Spanish Ministry of Industry, Tourism and Commerce and the Spanish Ministry of Education, respectively. Besides, the knowledge bases were populated using the COGNOS toolkit developed through the research project MA2VICMR (S2009/TIC-1542) supported by the Regional Government of Madrid.
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RD 30 may. 2024