Relation Extraction via Domain-aware Transfer Learning

Relation extraction in knowledge base construction has been researched for the last decades due to its applicability to many problems. Most classical works, such as supervised information extraction and distant supervision, focus on how to construct the knowledge base (KB) by utilizing the large number of labels or certain related KBs. However, in many real-world scenarios, the existing methods may not perform well when a new knowledge base is required but only scarce labels or few related KBs available. In this paper, we propose a novel approach called, Relation Extraction via Domain-aware Transfer Learning (ReTrans), to extract relation mentions from a given text corpus by exploring the experience from a large amount of existing KBs which may not be closely related to the target relation. We first propose to initialize the representation of relation mentions from the massive text corpus and update those representations according to existing KBs. Based on the representations of relation mentions, we investigate the contribution of each KB to the target task and propose to select useful KBs for boosting the effectiveness of the proposed approach. Based on selected KBs, we develop a novel domain-aware transfer learning framework to transfer knowledge from source domains to the target domain, aiming to infer the true relation mentions in the unstructured text corpus. Most importantly, we give the stability and generalization bound of ReTrans. Experimental results on the real world datasets well demonstrate that the effectiveness of our approach, which outperforms all the state-of-the-art baselines.