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

TL;DR

This paper introduces a novel entity matching framework that decouples feature learning from decision making, leveraging self-supervised learning and decision trees for interpretability and efficiency, outperforming state-of-the-art models.

Contribution

The proposed framework combines Heterogeneous Information Fusion and Key Attribute Tree induction to improve interpretability and resource efficiency in entity matching.

Findings

Outperforms SOTA models on multiple datasets

Efficient learning with limited annotated data

Provides interpretable matching rules

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.