Survey of Knowledge Graph Representation Learning for Relation Feature Modeling
Knowledge graph representation learning techniques can transform symbolic knowledge graphs into numerical repre-sentations of entities and relations,and then effectively combine various deep learning models to facilitate downstream applications of knowledge enhancement.In contrast to entities,relations fully embody semantics in knowledge graphs.Thus,modeling various characteristics of relations significantly influences the performance of knowledge graph representation learning.Firstly,aiming at the complex mapping properties of one-to-one,one-to-many,many-to-one,and many-to-many relations,relation-aware mapping-based models,specific representation space-based models,tensor decomposition-based models,and neural network-based models are reviewed.Next,focusing on modeling various relation patterns such as symmetry,asymmetry,inversion,and composition,we summarize models based on modified tensor decomposition,models based on modified relation-aware mapping,and models based on rotation operations.Subsequently,considering the implicit hierarchical relations among entities,we introduce auxiliary informa-tion-based models,hyperbolic spaces-based models,and polar coordinate system-based models.Finally,for more complex scenarios such as sparse knowledge graphs and dynamic knowledge graphs,this paper discusses some future research directions.It explores ideas like integrating multimodal information into knowledge graph representation learning,rule-enhanced relation patterns mo-deling,and modeling relation characteristics for dynamic knowledge graph representation learning.
万方数据
维普
