【摘要】 为了在海量文库知识图谱中快速提取出关键信息，提出学术文本关键词文库知识图谱实体关系抽取算法。通过优化完整策略的模糊C均值聚类(OCS-FCM:Optimization of Complete Strategy Fuzzy C-Means)和弹性嵌入t-分布随机邻域(E-t-SNE:Elastic-embedding t-Distributed Stochastic Neighbor Embedding)算法分别对文库中的关键词实施缺失值填补和降维，以学术文本关键词文库中的实体作为顶点，建立知识图谱。根据关键词的词性等特征，基于自注意力机制算法构建自注意力双向长短期记忆网络(SelfATT-BLSTM:Self-Attention Bidirectional Long Short-Term Memory)模型对知识图谱中的实体关系进行抽取，并获取实体抽取后的结果。实验结果表明，所提算法的采集精度始终在0.8以上，准确率(ACC:Accuracy)值高于30%,抽取时间未超过1.5 s,具有良好的实体关系抽取能力。在实体抽取过程中拥有极高的准确度和效率。更多还原

【Abstract】 In order to quickly extract key information from massive library knowledge graphs, an entity relationship extraction algorithm for academic text keyword library knowledge graphs is proposed. OCS-FCM(Optimization of Complete Strategy Fuzzy C-Means) and Elastic E-t-SNE(Embedding t-Distributed Stochastic Neighbor Embedding) algorithms are used to perform missing value filling and dimensionality reduction on key words in the library. And using entities in the academic text keyword library as vertices, a knowledge graph is established. Based on the part of speech and other features of keywords, a SelfATT BLSTM(Self Attention Bidirectional Long Short Term Memory) model is constructed using a self attention mechanism algorithm to extract entity relationships from the knowledge graph and obtain the extracted results. Experimental results have shown that the collection accuracy of proposed algorithm is more than 0.8, with an ACC(Accuracy) value over 30% and a extraction time less than 1.5 s, demonstrating excellent ability to extract entity relationships.更多还原