  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.
