Data mining-based approach for ontology matching problem

Abstract

Ontology matching aims at identifying the correspondences between instances and data properties of different ontologies. The use of data mining approach in matching ontology problem is reviewed in this article. We propose DMOM (Data Mining for Ontology Matching based instances) framework to select data properties of instances efficiently. The framework exploits data mining techniques to select the most appropriate features to match ontologies. Moreover, three strategies have been investigated to select the relevant features for the matching process. The first one called exhaustive, explores the enumerate search tree randomly by generating at each iteration a subset of feature attributes, where each node is evaluated by running the matching process on its selected attributes. The second approach called statistical, it uses some statistical values to select the most relevant properties. The third one called FIM (Frequent Itemsets Mining), it explores the correlation between different properties and selects the most frequent properties describing the overall instances of the given ontology. To demonstrate the usefulness of DMOM framework, several experiments have been carried out on OAEI (Ontology Alignment Evaluation Initiative) and DBpedia ontology databases. The results show that the third strategy, FIM, outperforms the two other strategies (Exhaustive, and Statistical). The results also reveal that DMOM outperforms the state-of-the-art ontology matching approaches in terms of execution time and the quality of the matching process.