计算机科学 ›› 2025, Vol. 52 ›› Issue (3): 287-294.doi: 10.11896/jsjkx.240700156
宋宝燕, 刘杭生, 单晓欢, 李素, 陈泽
SONG Baoyan, LIU Hangsheng, SHAN Xiaohuan, LI Su, CHEN Ze
摘要： 近年来,知识图谱嵌入(Knowledge Graph Embedding,KGE)作为一种主流方法在知识图谱补全任务中已取得显著效果。然而,现有KGE方法仅在数据层考虑三元组信息,忽略了不同三元组间在逻辑层存在的关系模式语义,导致现有方法仍存在一定性能缺陷。针对上述问题,提出一种融合关系模式和类比迁移的知识图谱补全方法(Fusing Relational-pattern and Ana-logy Transfer,RpAT)。首先,在逻辑层,根据实体关系的语义层次结构,细分为不同的关系模式;其次,在数据层,提出一种模式类比对象生成方法,该方法利用关系模式性质生成目标三元组相似类比对象,依据类比对象对缺失信息进行迁移;最后,提出一种融合了原始知识图谱嵌入模型的推理能力与类比迁移能力的综合性评分函数,以提升图谱补全性能。实验结果表明,在FB15k-237和WN18RR数据集上,相较于其他基线模型,RpAT方法的MRR值分别提升了15.5%和1.8%,验证了在知识图谱补全任务中的有效性。
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