Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision Making

• URL: http://arxiv.org/abs/2106.04174v1
• Date: Tue, 8 Jun 2021 08:27:31 GMT
• Title: Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision Making
• Authors: Zijun Yao, Chengjiang Li, Tiansi Dong, Xin Lv, Jifan Yu, Lei Hou, Juanzi Li, Yichi Zhang, Zelin Dai
• Abstract summary: Entity Matching aims at recognizing entity records that denote the same real-world object. We propose a novel EM framework that consists of Heterogeneous Information Fusion (HIF) and Key Attribute Tree (KAT) Induction. Our method is highly efficient and outperforms SOTA EM models in most cases.
• Score: 22.755892575582788
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Entity Matching (EM) aims at recognizing entity records that denote the same real-world object. Neural EM models learn vector representation of entity descriptions and match entities end-to-end. Though robust, these methods require many resources for training, and lack of interpretability. In this paper, we propose a novel EM framework that consists of Heterogeneous Information Fusion (HIF) and Key Attribute Tree (KAT) Induction to decouple feature representation from matching decision. Using self-supervised learning and mask mechanism in pre-trained language modeling, HIF learns the embeddings of noisy attribute values by inter-attribute attention with unlabeled data. Using a set of comparison features and a limited amount of annotated data, KAT Induction learns an efficient decision tree that can be interpreted by generating entity matching rules whose structure is advocated by domain experts. Experiments on 6 public datasets and 3 industrial datasets show that our method is highly efficient and outperforms SOTA EM models in most cases. Our codes and datasets can be obtained from https://github.com/THU-KEG/HIF-KAT.

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