Relation-Aware Entity Matching Using Sentence-BERT

Abstract

A key aspect of Knowledge fusion is Entity Matching. The objective of this study was to investigate how to identify heterogeneous expressions of the same real-world entity. In recent years, some representative works have used deep learning methods for entity matching, and these methods have achieved good results. However, the common limitation of these methods is that they assume that different attribute columns of the same entity are independent, and inputting the model in the form of paired entity records will cause repeated calculations. In fact, there are often potential relations between different attribute columns of different entities. These relations can help us improve the effect of entity matching, and can perform feature extraction on a single entity record to avoid repeated calculations. To use attribute relations to assist entity matching, this paper proposes the Relation-aware Entity Matching method, which embeds attribute relations into the original entity description to form sentences, so that entity matching is transformed into a sentence-level similarity determination task, based on Sentence-BERT completes sentence similarity calculation. We have conducted experiments on structured, dirty, and textual data, and compared them with baselines in recent years. Experimental results show that the use of relational embedding is helpful for entity matching on structured and dirty data. Our method has good results on most data sets for entity matching and reduces repeated calculations.

ContextAD: Context-Aware Acronym Disambiguation with Siamese BERT Network

“…Ten, we augment the embedding by |u − v|, which means subtracting the two vectors u and v in element-wise and calculating the absolute value. Te vectors u, v, and |u − v| are concatenated into the fully connected layer, followed by the Soft Max layer, to obtain the fnal predicted score [40]. Te objective function can be expressed as follows:…”