Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision Making (2021)

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 annotated 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. We will release the codes upon acceptance.

## introduction entity matching (em) aims at identifying whether two records from different sources refer to the same real-world entity. this is a fundamental research task in knowledge graph integration @xcite @xcite and text mining @xcite . in real applications, it is not easy to decide whether two records with ad hoc linguistic descriptions refer to the same entity. in figure 1 , e 2 and e 3 refer to the same publication, while e 1 refers to a different one. venues of e 2 and e 3 have different expressions; authors of e 3 is misplaced in its title field. early works include feature engineering @xcite and rule matching @xcite . recently, the robustness of entity matching has been improved by deep learning models, such as distributed representation based models @xcite , attention based models @xcite @xcite , and pre-trained language model based models @xcite . nevertheless, these modern neural em models suffer from two limitations as follows. low-resource training. supervised deep learning em relies on large amounts of labeled training data, which is extremely costly in reality. attempts have been made to leverage external data via transfer learning @xcite @xcite and pre-trained language model based methods @xcite . other attempts have also been made to improve labeling efficiency via active learning @xcite and crowdsourcing techniques @xcite . however, external information may introduce noises, and active learning and crowdsourcing still require additional labeling work. ## methodology in this section, we introduce (1) a neural model, heterogeneous information fusion (hif), for the task of feature representation, and (2) a decision ## related works the way of extracting comparison features falls into two categories: monotonic and non-monotonic. monotonic features are (negatively) proportional similarities between attribute values. they can be calculated by symbolic rules, such as jaccard similarity, levenshtein similarity @xcite @xcite , or learned from differentiable comparison operations, such as subtracting, point-wise multiplication @xcite @xcite . non-monotonic features are hidden representations of end-to-end neural networks, such as softmax or sigmoid based similarity scores @xcite , attention based scores @xcite , or simply embedding based features @xcite . em with limited resources has recently intrigued research interest @xcite . existing explorations seek solution from leveraging external data to improving annotation efficiency. external data can be aggregated via transfer learning @xcite @xcite , or via pre-training language models @xcite . for better annotations, researchers tried active learning @xcite @xcite , or crowd sourcing techniques @xcite . the interpretability of neural models will contribute to the trust and the safety. it has become one of the central issues in machine learning. @xcite examines interpretability in em risk analysis. there are also attempts to explain from the perspective of attention coefficients @xcite . ## conclusion we present a decoupled framework for interpretable entity matching. it is robust to both noisy heterogeneous input and the scale of training resources. experiments show that our method can be converted to interpretable rules, which can be inspect by domain experts and make em process more reliable. in the future, it is intriguing to explore more efficient ways to explore unlabeled data, such as levering connections among entities, or combine with pre-trained language models. it is also valuable to explore how to use our heterogeneous information fusion module to boost other em methods, such as injecting hif representation as supplementary information into end-to-end models.