Abstract
AbstractAs 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 cl...
Highlights
Ontology, as it represents a mean to formalize the domain knowledge, has become the enabler of the knowledge exchange between the heterogeneous data sources
The proposed approach has been evaluated with experimentations on real world mammographic ontologies which are open source, namely: -‘Breast Cancer Grading Ontology (BCGO)’ [19]: The BCGO ontology has been developed in 2009; it contains 364 classes, 156 properties and 164 individuals
It is designed to be application oriented ontology and addresses the problem of semantic gap between highlevel semantic concepts and the characteristics of the low-level image. -‘Gimi Mammography ontology’ [20]: The Gimi mammography ontology has been developed in 2012; it contains 310 classes and 135 properties, it is used to describe the richness and complexity of the domain and has been implemented with OWL 2, so to be integrated into a learning tool to compare the reviews of trainees with respect to the expert annotations
Summary
As it represents a mean to formalize the domain knowledge, has become the enabler of the knowledge exchange between the heterogeneous data sources. In the perspective of performing both speediness and effectiveness of the ontology alignment process, some researchers have tackled the problem of scalability with the use of the clustering algorithms Such technique aims to reduce the research space of correspondences between the ontologies’ entities to be aligned. The alignment process, parses both sets of produced clusters of the ontologies and exploits the whole cluster’s information to determine the similar clusters pairs having the higher proximity. This proximity is based on anchors (shared entities). A generic approach called FHCbM (Fuzzy Hierarchical clustering based method) based on the ontology knowledge mining is proposed to address the challenge of the increased concepts sets size to be treated. The medoid of the cluster ܥ, where ݒ, ܿ H ;ܥw.r.t.the semantic distance ݀(. ):
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