Improved strategies of relation extraction based on graph convolutional model on tree structure for web information processing

Abstract In the Industry 4.0/5.0 era, information integration is employed to fuse information from different companies to facilitate interoperation. However, information extraction is an important preprocessing phase that must be performed prior to integrating data from different contexts. Relation extraction, which is an element of information extraction, is typically the basis of many upper-level applications, e.g., information visualization and inference. Some current models may not fully consider the complementary effect of information at different levels of granularity featured by different neural networks. In this paper, two improvement relation extraction strategies based on the graph convolutional model on tree structure (GCNTree) are proposed. The first strategy integrates a hierarchical attention mechanism and correlation analysis between subjects and objects to generate sentence and entity vectors, respectively. The second strategy merges a named-entity recognition subnetwork with GCNTree to realize joint learning of relation and entity extraction. Experimental results demonstrate that the proposed strategies are comparable to state-of-the-art methods. 1