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Learning to Rerank Schema Matches
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DOI：10.1109/TKDE.2019.2962124.png)
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摘要
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Schema matching is at the heart of integrating structured and semi-structured data with applications in data warehousing, data analysis recommendations, Web table matching, etc. Schema matching is known as an uncertain process and a common method to overcome this uncertainty introduces a human expert with a ranked list of possible schema matches to choose from, known as top-K matching. In this work we propose a learning algorithm that utilizes an innovative set of features to rerank a list of schema matches and improves upon the ranking of the best match. We provide a bound on the size of an initial match list, tying the number of matches with a desired level of confidence in finding the best match. We also propose the use of matching predictors as features in a learning task, and tailored nine new matching predictors for this purpose. The proposed algorithm assists the matching process by introducing a quality set of alternative matches to a human expert. It also serves as a step towards eliminating the involvement of human experts as decision makers in a matching process altogether. A large scale empirical evaluation with real-world benchmark shows the effectiveness of the proposed algorithmic solution.
Keyword:
Schema matching
data integration
uncertainty
learning to rerank
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10.4
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