A Fuzzy Rule-Based System for Ontology Mapping

Authors:
Susel Fernández;Juan R. Velasco;Miguel A. López-Carmona
Affiliations:
Department of Automatic, University of Alcala. Edificio Politécnico, Madrid, Spain 600 28871.;Department of Automatic, University of Alcala. Edificio Politécnico, Madrid, Spain 600 28871.;Department of Automatic, University of Alcala. Edificio Politécnico, Madrid, Spain 600 28871.
Venue:
PRIMA '09 Proceedings of the 12th International Conference on Principles of Practice in Multi-Agent Systems
Year:
2009

Citing 8
Cited 0

Information flow: the logic of distributed systems

Information flow: the logic of distributed systems
Information Retrieval

Information Retrieval
A cooperative framework for integrating ontologies

International Journal of Human-Computer Studies
Promptdiff: a fixed-point algorithm for comparing ontology versions

Eighteenth national conference on Artificial intelligence
The PROMPT suite: interactive tools for ontology merging and mapping

International Journal of Human-Computer Studies
Ontology mapping: the state of the art

The Knowledge Engineering Review
OMEN: a probabilistic ontology mapping tool

ISWC'05 Proceedings of the 4th international conference on The Semantic Web
A bayesian network approach to ontology mapping

ISWC'05 Proceedings of the 4th international conference on The Semantic Web

Quantified Score

Hi-index	0.00

Visualization

Abstract

Ontologies are a crucial tool for formally specifying the vocabulary and the concepts of agent platforms, so, to share information, agents that use different vocabularies must be able to translate data from one ontological framework to another. The treatment of uncertainty plays a key role in the ontology mapping, as the degree of overlapping between concepts can not be represented logically. This paper aims to provide mechanisms to support experts in the first steps of the ontology mapping process using fuzzy logic techniques to determine the similarity between concepts from different ontologies. For each pair of concepts, two types of similarity are calculated: the first using the Jaccard coefficient, based on relevant documents taken from the web, and the second based on the linguistic relationship of concepts. Finally, the similarity is calculated through a fuzzy rule-based system. The ideas presented in this work are validated using two real-world ontologies.