Relations between entities can be represented by different instances, e.g., a
sentence 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 the
incompleteness of the knowledge source. In this paper, we introduce the Open
Relation Modeling problem - given two entities, generate a coherent sentence
describing the relation between them. To solve this problem, we propose to
teach machines to generate definition-like relation descriptions by letting
them learn from defining entities. Specifically, we fine-tune Pre-trained
Language Models (PLMs) to produce definitions conditioned on extracted entity
pairs. To help PLMs reason between entities and provide additional relational
knowledge to PLMs for open relation modeling, we incorporate reasoning paths in
KGs and include a reasoning path selection mechanism. Experimental results show
that our model can generate concise but informative relation descriptions that
capture the representative characteristics of entities.

本文介绍了开放式关系建模问题，引入了使用定义实体进行条件生成类型的关系描述的方法，并将推理路径和选择机制包括在内以帮助在知识图谱数据上进行开放式关系建模。实验结果表明，该模型能够生成简洁但信息丰富的关系描述，捕捉实体的代表性特征。