David Carmel, Haggai Roitman, et al.
ACM TIST
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.
David Carmel, Haggai Roitman, et al.
ACM TIST
Oren Sar Shalom, Haggai Roitman, et al.
ICTIR 2017
Haggai Roitman, Jonathan Mamou, et al.
CIKM 2012
Roee Shraga, Haggai Roitman, et al.
SIGIR 2020