大多现有知识图谱补全方法普遍忽略了实体间客观存在的语义层次差异,为解决该问题,提出一种在球面坐标系下基于语义分层信息的知识图谱补全(knowledge graph completion on semantic hierarchy in spherical coordinates,SpHKC)模型.该方法将实体映射到球面坐标,位于不同球面的实体处于不同语义层级,球的半径越大,该球面上的实体所位于的语义层级越低.而关系则被建模为一个球面的实体向另一实体(位于相同球面或不同球面)的移动,包含旋转与定位操作,以处理实体语义层级异同的两种情况.球面坐标的极角和方位角也给予实体更丰富的表达形式.实验表明,SpHKC与当前主流方法在FB15k-237和WN18RR数据集上的效果基本持平,并且它在YAGO3-10数据集的平均倒数排名(mean reciprocal ranking,MRR)、Hits@10等重要指标上比相关研究的最新算法稳定提升约1％,证明了语义分层信息的有效性.
Knowledge Graph Completion Method Based on Semantic Hierarchy in Spherical Coordinates
Most of existing knowledge graph completion methods often neglect the se-mantic hierarchical differences that objectively exist between entities.To address these limitations,we propose a knowledge graph completion method named spherical hierarchi-cal knowledge completion(SpHKC),which models semantic hierarchical phenomena using spherical coordinates.In this method,entities are mapped to points on a spherical sur-face,and entities located on different spheres correspond to different semantic hierarchy levels.The radius of the sphere determines the level of the semantic hierarchy for entities on that sphere,with larger spheres representing lower levels.Relationships are modeled as movements from one entity on the spherical surface to another entity(on the same or different spheres),involving rotation and positioning operations to handle both similar and different semantic hierarchy levels between entities.The polar angle and azimuth angle in spherical coordinates provide entities with richer expressions.Experimental results demon-strate that SpHKC achieves comparable performance to state-of-the-art methods on the FB15k-237 and WN18RR datasets.Moreover,it consistently improves important metrics such as MRR(mean reciprocal ranking)and Hits@10 by approximately 1％compared to recent algorithms on the YAGO3-10 dataset,showcasing the effectiveness of incorporating semantic hierarchical information.
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