Most of the existing knowledge graphs are not usually complete and can be complemented by some reasoning algorithms. The reasoning method based on path features is widely used in the field of knowledge graph reasoning and completion on account of that its have strong interpretability. However, reasoning methods based on path features still have several problems in the following aspects: Path search isinefficient, insufficient paths for sparse tasks and some paths are not helpful for reasoning tasks. In order to solve the above problems, this paper proposes a method called DC-Path that combines dynamic relation confidence and other indicators to evaluate path features, and then guide path search, finally conduct relation reasoning. Experimental result show that compared with the existing relation reasoning algorithm, this method can select the most representative features in the current reasoning task from the knowledge graph and achieve better performance on the current relation reasoning task.
翻译：现有大多数知识图表通常并不完整,可以某些推理算法加以补充。基于路径特征的推理方法在知识图表推理和完成方面广泛使用,因为其具有很强的可解释性。然而,基于路径特征的推理方法在以下几个方面仍有若干问题:路径搜索效率低下,稀少任务和一些路径不足,对推理任务没有帮助。为了解决上述问题,本文件建议了一种称为DC-Path的方法,这种方法将动态关系信心和其他指标结合起来,以评价路径特征,然后指导路径搜索,最后进行关系推理。实验结果表明,与现有的关系推理算法相比,这一方法可以从知识图表中选择当前推理任务中最具代表性的特征,并在当前关系推理任务上取得更好的表现。
相关内容
微信扫码咨询专知VIP会员