Title :
Aggregation of Ontology Matchers in Lieu of a Reference Ontology
Author :
Al Boni, Mohammad ; Anderson, Derek T.
Author_Institution :
Center for Adv. Vehicular Syst., Mississippi State Univ., Starkville, MS, USA
Abstract :
Ontologies are widely used to represent knowledge in different domains. As a result, numerous methods have been put forth to match ontologies. No technique has been shown to be robust across all domains. Furthermore, ontology matchers typically make use of a reference ontology. However, this is not guaranteed to exist. In this article, the fuzzy integral is used to aggregate multiple ontology matchers in lieu of a reference ontology. Specifically, we present a way to derive the fuzzy measure based on ideas from crowd sourcing when the worth of individuals is not known. Preliminary results are presented to show the robustness of our approach across different domains.
Keywords :
fuzzy set theory; knowledge representation languages; ontologies (artificial intelligence); crowd sourcing; fuzzy integral; knowledge representation language; ontology matcher aggregation; reference ontology; Frequency modulation; Measurement; Ontologies; Robustness; Sociology; Standards; Statistics; crowd sourcing; fuzzy integral; fuzzy measure; measure of agreement; ontology matching;
Conference_Titel :
Computational Science and Computational Intelligence (CSCI), 2014 International Conference on
Conference_Location :
Las Vegas, NV
DOI :
10.1109/CSCI.2014.67