Systematic Approach for Measuring Semantic Relatedness between Ontologies

Abstract

Measuring ontology matching is a critical issue in knowledge engineering and supports knowledge sharing and knowledge evolution. Recently, linguistic scientists have defined semantic relatedness as being more significant than semantic similarities in measuring ontology matching. Semantic relatedness is measured using synonyms and hypernym–hyponym relationships. In this paper, a systematic approach for measuring ontology semantic relatedness is proposed. The proposed approach is developed with a clear and fully described methodology, with illustrative examples used to demonstrate the proposed approach. The relatedness between ontologies has been measured based on class level by using lexical features, defining semantic similarity of concepts based on hypernym–hyponym relationships. For evaluating our proposed approach against similar works, benchmarks are generated using five properties: related meaning features, lexical features, providing technical descriptions, proving applicability, and accuracy. Technical implementation is carried out in order to demonstrate the applicability of our approach. The results demonstrate an achieved accuracy of 99%. The contributions are further highlighted by benchmarking against recent related works.

Ontology Driven for Mapping a Relational Database to a Knowledge-based System

“…There is no doubt that the relational database system is a vigorous technique for controlling and managing daily transaction systems. On the other hand, in a system requesting analysis for historical data, other data models such as non-SQL, graphic, and knowledge-based systems, and ontologies could provide more benefits than the relational database [24]. As has been proved, the relational database is a useful structural technique for the OLTP system, where data insertion and data integrity are important issues.…”