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Relations between entities can be represented by different instances, e.g., asentence containing both entities or a fact in a Knowledge Graph (KG). However,these instances may not well capture the general relations between entities,may be difficult to understand by humans, even may not be found due to theincompleteness of the knowledge source. In this paper, we introduce the OpenRelation Modeling problem - given two entities, generate a coherent sentencedescribing the relation between them. To solve this problem, we propose toteach machines to generate definition-like relation descriptions by lettingthem learn from defining entities. Specifically, we fine-tune Pre-trainedLanguage Models (PLMs) to produce definitions conditioned on extracted entitypairs. To help PLMs reason between entities and provide additional relationalknowledge to PLMs for open relation modeling, we incorporate reasoning paths inKGs and include a reasoning path selection mechanism. Experimental results showthat our model can generate concise but informative relation descriptions thatcapture the representative characteristics of entities.

Open Relation Modeling: Learning to Define Relations between Entities