When it comes to comprehending and analyzing multi-relational data, the
semantics of relations are crucial. Polysemous relations between different
types of entities, that represent multiple semantics, are common in real-world
relational datasets represented by knowledge graphs. For numerous use cases,
such as entity type classification, question answering and knowledge graph
completion, the correct semantic interpretation of these relations is
necessary. In this work, we provide a strategy for discovering the different
semantics associated with abstract relations and deriving many sub-relations
with fine-grained meaning. To do this, we leverage the types of the entities
associated with the relations and cluster the vector representations of
entities and relations. The suggested method is able to automatically discover
the best number of sub-relations for a polysemous relation and determine their
semantic interpretation, according to our empirical evaluation.

本文提出了一种发现抽象关系中不同语义和获取细致意义的子关系的策略，其方法基于关系涉及的实体类型和向量表示的聚类，能够自动发现多义关系的最佳子关系及其语义解释。