论文标题
基于动态关系置信度在知识图中发现重要路径
Discover Important Paths in the Knowledge Graph Based on Dynamic Relation Confidence
论文作者
论文摘要
大多数现有的知识图通常都不完整，可以通过某些推理算法来补充。基于路径特征的推理方法被广泛用于知识图推理的领域，并且由于其具有强大的解释性。但是，基于路径特征的推理方法在以下方面仍然存在多个问题：搜索search iSineff，稀疏任务的路径不足，某些路径对推理任务无济于事。为了解决上述问题，本文提出了一种称为DC路径的方法，该方法结合了动态关系置信度和其他指标，以评估路径特征，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，此方法可以从知识图中选择当前推理任务中最具代表性的功能，并在当前关系推理任务上实现更好的性能。
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.
