Ontology Knowledge Mining for Ontology Alignment

Abstract

As the ontology alignment facilitates the knowledge exchange among the heterogeneous data sources, several methods have been introduced in literature. Nevertheless, few of them have been interested in decreasing the problem complexity and reducing the research space of correspondences between the input ontologies.This paper presents a new approach for ontology alignment based on the ontology knowledge mining. The latter consists on producing for each ontology a hierarchical structure of fuzzy conceptual clusters, where a concept can belong to several clusters simultaneously. Each level of the hierarchy reflects the knowledge granularity degree of the knowledge base in order to improve the effectiveness and speediness of the information retrieval. Actually, such method allows the knowledge granularity analyze between the ontologies and facilitates several ontology engineering techniques. The ontology alignment process is performed iteratively over the produced hierarchical structure of the fuzzy clusters using semantic techniques. Once the correspondent clusters are identified, we consider both syntactic and structural characteristics of their correspondent entities. The proposed approach has been tested over the OAEI benchmark dataset and some real mammographic ontologies since this work is a part of CMCU project for Mammographic images analysis for Assistance Diagnostic Breast Cancer. The system performs good results in the terms of precision and recall with respect to other alignment system.

Ontology negotiation: Knowledge interchange between distributed ontologies through agent negotiation

“…To improve the performance of ontology alignment, Xue et al 11 proposed a novel approach based on compact genetic algorithm, which is able to reduce the time and memory consumption as well as to ensure the completeness and correctness. An ontology knowledge mining method for ontology alignment is put forward by Idoudi R 12 to address the problem complexity. Such method allows the knowledge granularity to be analyzed between ontologies and facilitates several ontology engineering techniques.…”