Avg. confidence on FB15K-237, by rule length T.

Representation learning on a knowledge graph (KG) aims to map entities and relationships into a low-dimensional vector space. Traditional methods for representation learning have predominantly focused on the structural aspects of triples within the KG. While existing approaches have endeavored to integrate path information and rules to enhance the structural richness of KGs, these efforts have been constrained by the lack of consideration for complex relational representations and contextual information. In this study, we introduce TP-RotatE, an innovative method that leverages the semantic context of triples to effectively capture more intricate relational patterns. Specifically, our model harnesses contextual information surrounding the head entity and distills relevant rules. These rules are then integrated with path information to offer a more holistic perspective on the relationships embedded within complex vector spaces. Furthermore, the synergy between rules and paths empowers the knowledge-embedded model to handle the intricacies of complex relationships. Experimental results on a benchmark dataset confirm that TP-RotatE surpasses current baseline methods in KG inference tasks, achieving state-of-the-art performance.知识图谱（Knowledge Graph，以下简称KG）表示学习旨在将实体与关系映射至低维向量空间。传统KG表示学习方法大多聚焦于图谱内三元组的结构属性。尽管现有研究已尝试整合路径信息与规则以提升KG的结构丰富度，但此类研究仍受限于未充分考量复杂关系表示与上下文信息。本研究提出TP-RotatE这一创新方法，该方法利用三元组的语义上下文，可有效捕捉更为复杂的关系模式。具体而言，本模型利用头实体周边的上下文信息并提炼相关规则，随后将这些规则与路径信息进行整合，从而对复杂向量空间中蕴含的关系形成更为全面的认知。此外，规则与路径的协同效应可赋能该知识嵌入模型，使其能够处理复杂关系的错综复杂之处。在基准数据集上的实验结果表明，TP-RotatE在KG推理任务中优于现有基线方法，实现了最先进的性能。