Knowledge Graph Enhanced Sentential Relation Extraction via Dual Heterogeneous Graph Context Selection

Bo Xu, Nian Liu, Luyi Cheng, Shizhou Huang, Shouang Wei, Ming Du, Hui Song, Hongya Wang

Abstract: Sentential relation extraction is a type of relation extraction task whose goal is to extract semantic relations between entities from a single sentence. Compared with other variants of relation extraction, it often suffers from limitations of semantic contextual information. Due to the presence of knowledge graphs, many approaches propose to augment the semantics of sentences with the knowledge of entities, thus improving the performance of relation extraction. Despite their success, existing methods still suffer from two weaknesses: (1) existing approaches aggregate sentences, entities and their attribute values into a heterogeneous information graph, but do not consider the types of edges; (2) existing methods dynamically select knowledge based only on the structural features of the graph, without considering the features of the nodes themselves. To address these two problems, we propose a dual heterogeneous graph context selection method for knowledge graph enhanced sentential relation extraction. Specifically, to solve the first problem, we employ an edge-aware graph convolutional network to learn the representations of the heterogeneous graph with considering the types of edges. To solve the second problem, we propose dual graph context selection to select the useful context by considering the graph structure and node feature representation together. Experiments conducted on the Wikidata-RE dataset demonstrate the effectiveness of the method.