论文标题
在知识图关系中发现细颗粒语义
Discovering Fine-Grained Semantics in Knowledge Graph Relations
论文作者
论文摘要
在理解和分析多关系数据时，关系的语义至关重要。不同类型的实体（代表多种语义）之间的多义关系在知识图表示的现实关系数据集中很常见。对于许多用例，例如实体类型分类，问题答案和知识图的完成，对这些关系的正确语义解释是必要的。在这项工作中，我们提供了一种策略，以发现与抽象关系相关的不同语义，并得出许多具有细粒度含义的子关系。为此，我们利用与关系相关的实体的类型，并将实体和关系的向量表示。根据我们的经验评估，建议的方法能够自动发现多义关系的最佳亚关系并确定其语义解释。
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.
