2021
DOI: 10.48550/arxiv.2112.02792
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Incentive Compatible Pareto Alignment for Multi-Source Large Graphs

Abstract: In this paper, we focus on learning effective entity matching models over multi-source large-scale data. For real applications, we relax typical assumptions that data distributions/spaces, or entity identities are shared between sources, and propose a Relaxed Multi-source Large-scale Entity-matching (RMLE) problem. Challenges of the problem include 1) how to align large-scale entities between sources to share information and 2) how to mitigate negative transfer from joint learning multi-source data. What's wor… Show more

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