Open Hierarchical Relation Extraction (2021)

Abstract: Open relation extraction (OpenRE) aims to extract novel relation types from open-domain corpora, which plays an important role in completing the relation schemes of knowledge bases (KBs). Most OpenRE methods cast different relation types in isolation without considering their hierarchical dependency. We argue that OpenRE is inherently in close connection with relation hierarchies. To establish the bidirectional connections between OpenRE and relation hierarchy, we propose the task of open hierarchical relation extraction and present a novel OHRE framework for the task. We propose a dynamic hierarchical triplet objective and hierarchical curriculum training paradigm, to effectively integrate hierarchy information into relation representations for better novel relation extraction. We also present a top-down hierarchy expansion algorithm to add the extracted relations into existing hierarchies with reasonable interpretability. Comprehensive experiments show that OHRE outperforms state-of-the-art models by a large margin on both relation clustering and hierarchy expansion.

## ohre framework we divide the open hierarchical relation extraction problem into two phases: (1) learning relation representations with hierarchical information and (2) clustering and linking novel relations to existing hierarchies. ## experiments to verify the effectiveness of hierarchical information and ohre, we conduct comprehensive experiments on relation clustering and hierarchy expansion on two real-world datasets. we also conduct a detailed analysis of ohre to provide a better understanding of our framework. we refer readers to the appendix for more implementation details. ## conclusion in this work, we make the first attempt to address bidirectional connections between openre and relation hierarchy. in the future, we believe the following directions worth exploring: (1) we use a heuristic method to add new relations into hierarchies based on local similarities between relations. in future, more advanced methods can be designed to model the global interaction between new relations and hierarchy, and learn to effectively add the novel relations. (2) we conduct relation representation learning and hierarchy expansion in a pipeline. in the future, end-to-end models can be developed to jointly optimize these important phases for better open hierarchical relation extraction results. 6 acknowledgement this work is supported by the national key research and development program of china (no. 2020aaa0106501). yao is also supported by 2020 tencent rhino-bird elite training program.