ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2019, Vol. 56 ›› Issue (12): 2536-2548.doi: 10.7544/issn1000-1239.2019.20190640

Special Issue: 2019大数据知识工程及应用专题

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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

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

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