Abstract
Matching schemas at an element level or structural level is generally categorized as either hybrid, which uses one algorithm, or composite, which combines evidence from several different matching algorithms for the final similarity measure. We present an approach for combining element-level evidence of similarity for matching XML schemas with a composite approach. By combining high recall algorithms in a composite system we reduce the number of real matches missed. By performing experiments on a number of machine learning models for combination of evidence in a composite approach and choosing the SMO for the high precision and recall, we increase the reliability of the final matching results. The precision is therefore enhanced (e.g., with data sets used by Cupid and suggested by the author of LSD, our precision is respectively 13.05% and 31.55% higher than COMA and Cupid on average).
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Hong-Minh, T., Smith, D. (2006). Machine Learning Models: Combining Evidence of Similarity for XML Schema Matching. In: Nayak, R., Zaki, M.J. (eds) Knowledge Discovery from XML Documents. KDXD 2006. Lecture Notes in Computer Science, vol 3915. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11730262_7
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DOI: https://doi.org/10.1007/11730262_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33180-3
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