Abstract
Rising adoption of mediation systems leading to plenty of room for improvement. As a matter of fact, schema matching is one of the biggest pressing challenges facing the improvement of mediation systems. The purpose of this research paper is to take aim at schema matching for mediation systems with a semantic similarity measure, which is highly expected to outperform the most well-known semantic similarity measures out there. Using WordNet information, this research study introduced a new semantic similarity measure (as well as a pre-matching strategy) and compared its performance against all the following measures: Resnik’s measure, Jiang and Conrath’s distance, Lin’s measure, and Nababteh’s measure. The results indicated that the new measure provides much better results than the aforementioned measures. This paper definitely solves the problem regarding schema matching for mediation systems, and even though it is still in an early stage, the new measure will benefit both researchers and organizations.
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Yousfi, A., Elyazidi, M.H., Zellou, A. (2018). Assessing the Performance of a New Semantic Similarity Measure Designed for Schema Matching for Mediation Systems. In: Nguyen, N., Pimenidis, E., Khan, Z., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2018. Lecture Notes in Computer Science(), vol 11055. Springer, Cham. https://doi.org/10.1007/978-3-319-98443-8_7
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