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View all- Brinkmann AShraga RBizer C(2024)SC-Block: Supervised Contrastive Blocking Within Entity Resolution PipelinesThe Semantic Web10.1007/978-3-031-60626-7_7(121-142)Online publication date: 26-May-2024
Sparkly: A Simple yet Surprisingly Strong TF/IDF Blocker for Entity Matching
Pages 1507 - 1519
Abstract
Blocking is a major task in entity matching. Numerous blocking solutions have been developed, but as far as we can tell, blocking using the well-known tf/idf measure has received virtually no attention. Yet, when we experimented with tf/idf blocking using Lucene, we found it did quite well. So in this paper we examine tf/idf blocking in depth. We develop Sparkly, which uses Lucene to perform top-k tf/idf blocking in a distributed share-nothing fashion on a Spark cluster. We develop techniques to identify good attributes and tokenizers that can be used to block on, making Sparkly completely automatic. We perform extensive experiments showing that Sparkly outperforms 8 state-of-the-art blockers. Finally, we provide an in-depth analysis of Sparkly's performance, regarding both recall/output size and runtime. Our findings suggest that (a) tf/idf blocking needs more attention, (b) Sparkly forms a strong baseline that future blocking work should compare against, and (c) future blocking work should seriously consider top-k blocking, which helps improve recall, and a distributed share-nothing architecture, which helps improve scalability, predictability, and extensibility.
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Published: 01 February 2023
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View all- Brinkmann AShraga RBizer C(2024)SC-Block: Supervised Contrastive Blocking Within Entity Resolution PipelinesThe Semantic Web10.1007/978-3-031-60626-7_7(121-142)Online publication date: 26-May-2024
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