基于混合增强智能的知识图谱推理技术研究

如今,知识图谱被广泛应用在各个领域,例如问答系统、推荐系统等。而基于知识图谱的应用表现很大程度上依赖于知识图谱本身的知识完备性与准确性。单纯通过人工补齐与审核的方式来构建知识图谱已无法满足超大规模知识图谱的需求。针对上述问题,提出一种基于混合增强智能的知识图谱推理框架,即同时利用机器模型与人的知识信息来完成知识图谱推理。该框架在基于知识图谱嵌入的向量空间中,利用混合增强智能模型来寻找到实体节点之间的有效路径。与现有方法不同的是,该方法在训练模型时,高效地利用人的知识信息来指导模型的优化。实验表明,该框架在公开数据集上的表现相较于现有方法有一定提升。

KNOWLEDGE GRAPH REASONING BASED ON HYBRID-AUGMENTED INTELLIGENCE

Nowadays,knowledge graph is widely used in various fields,such as question answering system,recommendation system and so on.The performance of application based on knowledge graph depends on the completeness and accuracy of knowledge graph.It is impossible to construct knowledge graph by manual completion and auditing to meet the needs of large-scale knowledge graphs.To address these challenges,we proposed a knowledge map reasoning framework based on hybrid-augmented intelligence.It uses machine model and human knowledge information to complete knowledge map reasoning simultaneously.The framework used hybrid-augmented intelligent model to find effective paths between entities in vector space based on knowledge graph embedding for knowledge reasoning.Different from the existing methods,this method made efficient use of human knowledge information to guide the optimization of the training model.Experiments on public datasets demonstrated the improvement of the proposed framework compared to existing methods.