Affiliations: [a] School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran | [b] Department of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands
Correspondence:
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Corresponding author: Hossein Rahmani, School of Computer Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran. E-mail: [email protected].
Abstract: Entity resolution refers to the process of identifying, matching, and integrating records belonging to unique entities in a data set. However, a comprehensive comparison across all pairs of records leads to quadratic matching complexity. Therefore, blocking methods are used to group similar entities into small blocks before the matching. Available blocking methods typically do not consider semantic relationships among records. In this paper, we propose a Semantic-aware Meta-Blocking approach called SeMBlock. SeMBlock considers the semantic similarity of records by applying locality-sensitive hashing (LSH) based on word embedding to achieve fast and reliable blocking in a large-scale data environment. To improve the quality of the blocks created, SeMBlock builds a weighted graph of semantically similar records and prunes the graph edges. We extensively compare SeMBlock with 16 existing blocking methods, using three real-world data sets. The experimental results show that SeMBlock significantly outperforms all 16 methods with respect to two relevant measures, F-measure and pair-quality measure. F-measure and pair-quality measure of SeMBlock are approximately 7% and 27%, respectively, higher than recently released blocking methods.
Keywords: Data matching, entity resolution, meta-blocking, word embedding, locality-sensitive hashing, semantic similarity, big data integration
