Attentive interaction-driven entity resolution over multi-source web information.

The task of multi-source web entity resolution (MSWER) aims to automatically discover entity references from multiple web sources that refer to the same real-world entity, which plays an important role in tasks such as question answering and recommendations. However, existing approaches typically suffer from three major limitations: (1) they usually treat the MSWER as an information retrieval task and focus on learning the similarity between entity references based on the associated features extracted from multiple sources; (2) they ignore the valuable implicit interactions between the associated features of different entities that cannot be directly captured based on the given data without any external knowledge; (3) they didn’t consider the redundant and noisy interactions between features. To overcome these limitations, this paper presents a novel attentive interaction-driven entity resolution model (AIDER). Our theme is to capture both the explicit and implicit interactions of features associated with entity references in the form of paths, and further develop an end-to-end entity resolution model for inferring the equivalent entity references. Accordingly, an external knowledge base is leveraged to construct paths for implicit interactions, and a well-designed attention mechanism is further employed to measure the importance of each path-based interaction, which focuses on useful interactions and neglects those redundant and noisy ones. Experimental results on three real-world datasets demonstrate that AIDER outperforms the state-of-the-art approaches.