一种基于图卷积自编码模型的多维度学科知识网络融合方法

目的/意义] 针对包含单一类型知识单元的知识网络难以全面反映学科知识结构的问题,提出一种从多维度进行知识网络结构融合的方法,为学科领域知识结构挖掘提供借鉴。方法/过程] 利用LDA及TF-IDF方法抽取学科知识单元,然后运用语义相似度和关键词共现分析方法构建3个学科知识子网络：主题网络、关键词网络和实体网络,并采用空间节点传递对齐方法对齐子网络节点,接着设计基于图卷积操作的自编码模型对知识节点进行表示,最后通过计算余弦相似度重构学科知识网络。结果/结论] 实验部分以人工智能领域为例,构建融合主题、关键词和实体的学科知识网络并展开分析,实验结果表明,本文所提方法能有效地揭示学科领域研究内容和知识结构,为学科知识发现与组织研究提供有益参考。

Multi-Dimensional Subject Knowledge Network Fusion Method Based on Graph Convolution Self-Encoding Model

Purpose/significance] Aiming at the problem that the knowledge network containing a single type of knowledge unit cannot fully reflect the knowledge structure of the subject, a method of integrating knowledge network structure in different dimensions is proposed to provide a reference for the knowledge structure mining in the subject area.Method/process] This paper used LDA and TF-IDF methods to extract subject knowledge units, and then used semantic similarity and keywords co-occurrence analysis methods to construct three subject knowledge sub-networks: topics network, keywords network and entities network, and adopted spatial nodes transfer alignment align the nodes of the sub-networks, then designed a self-encoding model based on the graph convolution operation to represent the knowledge nodes, and finally reconstructed the disciplinary knowledge network by calculating the cosine similarity.Result/conclusion] The experimental part takes the field of artificial intelligence as an example to construct a subject knowledge network that integrates topics, keywords, and entities and conducts analysis. The experimental results show that the method proposed in this article can effectively reveal the research content and knowledge structure of the subject area, and provide a useful reference for the discovery and organizational research of subject knowledge.