Abstract
Recently large amounts of schema data, which describe data structure of various domains such as purchase order, health, publication, geography, agriculture, environment and music, are available over the Web. Schema mapping aims to solve schema heterogeneity problem in schema data. This research thoroughly examines how string similarity metrics and text processing techniques impact on the performance of terminological schema mapping and highlights their limitations. Our experimental study demonstrates that the performance of terminological schema matching is significantly improved by using text processing techniques. However, the performance improvement is slightly different between datasets because of the characteristics of the datasets, and in spite of applying all text processing techniques, some datasets still exhibit low performance. Our research supports the claim that a system which can manage the context dependent characteristics of terminological schema matching is essential for better schema mapping algorithms.
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Anam, S., Kim, Y.S., Kang, B.H., Liu, Q. (2014). Evaluation of Terminological Schema Matching and Its Implications for Schema Mapping. In: Pham, DN., Park, SB. (eds) PRICAI 2014: Trends in Artificial Intelligence. PRICAI 2014. Lecture Notes in Computer Science(), vol 8862. Springer, Cham. https://doi.org/10.1007/978-3-319-13560-1_45
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DOI: https://doi.org/10.1007/978-3-319-13560-1_45
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