Open Relation Modeling: Learning to Define Relations between Entities Jie Huang Kevin Chen-Chuan Chang University of Illinois at Urbana-Champaign University of Illinois at Urbana-Champaign USA USA
[email protected] [email protected] Jinjun Xiong Wen-mei Hwu University at Buffalo University of Illinois at Urbana-Champaign USA USA
[email protected] [email protected] ABSTRACT while deep learning is used heavily recently for natural language Relations between entities can be represented by different instances, processing. e.g., a sentence containing both entities or a fact in a Knowledge Besides, even relations are represented, they may not be inter- Graph (KG). However, these instances may not well capture the pretable to humans. There are different ways to represent relations general relations between entities, may be difficult to understand between entities. For example, if two entities co-occur in a sentence, by humans, even may not be found due to the incompleteness of they are possibly related and the relation can be implied by the sen- the knowledge source. tence. From a structured perspective, a relation can be represented In this paper, we introduce the Open Relation Modeling task– as a fact or a multi-hop reasoning path between two entities in a given two entities, generate a coherent sentence describing the Knowledge Graph (KG). However, for humans without too much relation between them. To solve this task, we propose to teach ma- prior knowledge about the entities, it is still difficult to understand chines to generate definition-like relation descriptions by letting the relations by reading them.