ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2019, Vol. 56 ›› Issue (12): 2536-2548.doi: 10.7544/issn1000-1239.2019.20190640

所属专题: 2019大数据知识工程及应用专题

• 其他应用技术 • 上一篇    下一篇



  1. 1(国防科技大学信息系统工程重点实验室 长沙 410073);2(地球空间信息技术协同创新中心(武汉大学) 武汉 430079);3(长沙商贸旅游职业技术学院经济贸易学院 长沙 410073) (
  • 出版日期: 2019-12-01
  • 基金资助: 

End-to-end Knowledge Triplet Extraction Combined with Adversarial Training

Huang Peixin1, Zhao Xiang1,2, Fang Yang1, Zhu Huiming1,3, Xiao Weidong1,2   

  1. 1(Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073);2(Collaborative Innovation Center of Geospatial Technology(Wuhan University), Wuhan 430079);3(College of Economics and Trade, Changsha Commerce & Tourism College, Changsha 410073)
  • Online: 2019-12-01

摘要: 知识图谱作为一种有效表示现实世界的系统受到学术界和工业界广泛关注,并由于其精准表示知识的能力被广泛应用于信息服务、智慧搜索、自动问答等上层应用.知识图谱的核心为三元组形式的实体和关系.现有知识图谱远不足以描述现实世界,因此,如何通过实体关系抽取方法来补全或者构建新的知识图谱显得至关重要.传统流水线式的实体关系抽取方法会导致误差传递,而已有的联合抽取没有充分考虑命名实体识别与关系抽取之间的联系,从而降低抽取效果.针对上述问题,对知识三元组抽取方法进行了深入研究,提出了一种融合对抗训练的端到端知识三元组联合抽取方法.首先,采用了一种实体关系联合标注策略,通过端到端的神经网络抽取文本语义特征,并对文本进行自动标注;其次,模型在神经网络中加入自注意力机制增强对文本信息的编码能力,并通过引入带偏置项的目标函数提高对相关联实体的辨识能力;最后,模型融合了对抗训练以提高鲁棒性,改进抽取效果.在实验部分,采用4种分析方法和3种评价指标对模型性能进行评价分析,实验结果证明了模型在知识抽取上的性能明显优于现有方法.

关键词: 知识图谱, 知识三元组抽取, 对抗训练, 端到端网络, 标注策略

Abstract: As a system to effectively represent the real world, knowledge graph has been widely concerned by academia and industry, and its ability to accurately represent knowledge is widely used in upper applications such as information service, intelligent search, and automatic question answering. A fact (knowledge) in form of triplet (head_entity, relation, tail_entity), is the basic unit of knowledge graph. Since facts in existing knowledge graphs are far from enough to describe the real world, acquiring more knowledge for knowledge graph completion and construction appears to be crucial. This paper investigates the problem of knowledge triplet extraction in the task of knowledge acquisition. This paper proposes an end-to-end knowledge triplet extraction method combined with adversarial training. Traditional techniques, whether pipeline or joint extraction, failed to discover the link between two subtasks of named entity recognition and relation extraction, which led to error propagation and worse extraction effectiveness. To overcome these flaws, in this paper, we adopt an entity and relation joint tagging strategy, and leverage an end-to-end framework to automatically tag the text and classify the tagging results. In addition, self-attention mechanism is added to assist the encoding of text, an objective function with bias term is additionally introduced to increase the attention of relevant entities, and the adversarial training is utilized to improve the robustness of the model. In experiments, we evaluate the proposed knowledge triplet extraction model via three evaluation metrics and analyze the experiments in four aspects. The experimental results verify that our model outperforms other state-of-the-art alternatives on knowledge triplet extraction.

Key words: knowledge graph, knowledge triplet extraction, adversarial training, end-to-end network, tagging scheme