A novel approach for merging ontologies using formal concept analysis

Abstract

Ontologies are mainly used for knowledge sharing and also as a knowledge structure. Due to the rising nature of ontologies, the method of merging information in the corporate realm turns to be critical. In the existing methods, formal concept analysis does not provide an efficient pseudo-intent calculation and does not handle large context. The proposed technique focused the issue of ontology heterogeneity that blocks the ontology interoperability and proposed a novel algorithm called pseudo-intent with backtracking-based FCA-Merge. The pseudo-intent with backtracking-based FCA-Merge algorithm performs four phases to merge the given two ontologies. In the first phase, it obtains the perfect attribute for the matching object using decision tree and pseudo intent technique. In the second phase, the obtained results are warehoused in the linked list as a formal context. In the third phase, the perfect relationship among formal contexts from the linked list has been identified using backtracking techniques. Finally, the merging phase performs the merging between the identified relations. The experimental outcome shows that the pseudo-intent with backtracking-based FCA-Merge provides 97% of precision, 82% of recall and 89% of accuracy which is better than the existing technique.