DL+:一种增强型双层知识图谱推理框架

DL+:An Enhanced Double-layer Framework for Knowledge Graph Reasoning

知识图谱是图数据库的一个重要研究领域,它可以形式化地描述现实世界中的事物及其关系,但其不完整性和稀疏性阻碍了其在诸多领域中的应用。知识图谱推理技术旨在根据知识图谱中已有的知识来推断新的知识或识别错误的知识以完善知识图谱。尽管现有的各类推理方法可以获得部分有效知识,但仍然存在获取路径不全、忽略局部信息和引入噪声等问题。基于此,发现且明确提出路径连通性差问题并证明推理有效性与实体间路径连通比率呈正相关规律,进一步提出一种用于增强现有推理方法性能的双层框架DL+。模型第一层是知识增广器,主要利用社区发现算法在初始知识图谱上提取实体邻域信息,构建新知识以增广知识规模,然后设计社区剪枝优化去除构建时引入的噪声,最后将增广后的知识图谱抽取还原为与初始图谱表示相同的结构并输出到第二层以保证模型“即插即用”的特性。第二层是知识推理机,通过在知识增广后的图谱上进行学习推理以达到增强现有知识图谱推理模型的目的,使模型可以在图谱路径连通性比率较高的情况下获得更优的推理结果。最终在4个标准知识图谱数据集上进行的大量实验结果表明DL+算法可以有效缓解实体间路径连通性差的问题,与9类基准方法相比,所提算法的预测精度平均提高了4.798%。

As an important research field of the graph database,the knowledge graph(KG)can formally describe things and their relationships in the real world.However,its incompleteness and sparsity hinder its application in many fields.The knowledge graph reasoning(KGR)technology aims to complete the knowledge graph by inferring new knowledge or identifying wrong knowledge according to the existing knowledge in the knowledge graph.Although existing reasoning methods can obtain partially effective knowledge paths,there are still some problems such as incomplete acquisition paths,ignoring local information,introducing noise.Based on this,this paper finds and explicitly proposes the problem of poor path connectivity,proves that the reasoning validity is positively correlated with the path connectivity ratio between entities,and further proposes a double-layer framework DL+which is used to enhance the performance of existing reasoning methods.The first layer is a knowledge augmenter,which mainly uses the community discovery algorithm to extract the entity neighborhood information on the initial KG and build new knowledge to expand the knowledge scale,and then designs a community pruning optimization method to remove the noise introduced in the construction.Finally,the augmented KG is extracted and restored to the same structure as the initial KG representation and output to the second layer to ensure the“plug-and-play”feature of the model.The second layer is a knowledge reasoner,which can enhance the existing KGR model by learning and reasoning on the KG after knowledge augmentation,so that the model can obtain better reasoning results when the graph path connectivity ratio is high.Finally,a large number of experimental results on four standard KG datasets show that the DL+can effectively alleviate the problem of poor path connectivity between entities,and improve the average prediction accuracy by 4.798%compare with nine types of benchmark methods.