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数据分析与知识发现  2020, Vol. 4 Issue (10): 1-13     https://doi.org/10.11925/infotech.2096-3467.2020.0383
  综述评介 本期目录 | 过刊浏览 | 高级检索 |
领域事件图谱构建方法综述*
王毅1,沈喆1,姚毅凡1,成颖1,2()
1南京大学信息管理学院 南京 210023
2山东师范大学文学院 济南 250014
Domain-Specific Event Graph Construction Methods:A Review
Wang Yi1,Shen Zhe1,Yao Yifan1,Cheng Ying1,2()
1School of Information Management, Nanjing University, Nanjing 210023, China
2School of Chinese Language and Literature, Shandong Normal University, Jinan 250014, China
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摘要 

【目的】 分析并评述领域事件知识图谱构建的相关研究,为后续研究提供理论基础和实践指导。【文献范围】 利用Web of Science核心数据库和Google Scholar以“Event Graph”、“Event extraction”和“Event relation”等主题词进行检索,经过手工筛选获得代表性文献61篇。【方法】 采用文献调研方法系统梳理领域事件图谱在定义、构建流程、识别方法等方面的工作。总结了基于规则、基于特征学习以及基于神经网络三种事件抽取的方法,对事件抽取与事件关系抽取中的特征选择、模型架构以及实验结果等进行分析和对比。【结果】 借鉴通用图谱构建的方法,提出包括事件触发词识别、事件要素识别、事件关系识别以及事件存储等在内的领域事件图谱构建流程模型。从描述结构、领域限制、事件形式、推理能力和时序关系等角度阐明构建标准应具备的元素。在构建实践中,事件本体的借鉴和复用是必要选项,事件抽取采用神经网络方法是目前最优的选择。【局限】 由于标准数据集的缺失,事件关系抽取对比中未能采用统一的数据集进行量化比较。【结论】 提出从知识提升、迁移学习以及认知模型等三个视角开展该主题后继研究的建议。

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王毅
沈喆
姚毅凡
成颖
关键词 领域事件图谱知识图谱事件抽取信息提取    
Abstract

[Objective] This paper reviews construction methods for domain-specific event graphs, aiming to facilitate future research.[Coverage] We searched “Event Graph”, “Event extraction” and “Event relation” with Web of Science and Google Scholar, then retrieved a total of 61 representative literature.[Methods] We summarized the definition, construction process and extraction methods with literature review. Then, we discussed the rule-based, feature learning based, and neural network-based extraction techniques. Finally, we analyzed their feature selection procedures, model architecture and experiment results.[Results] Refer to the general knowledge graph construction methods, we proposed a process model that include trigger argument and relation recognition. We briefly described on construction standard in structure, domain, event form, inference ability and temporal relations. In practice, we found that Ontology reuse is necessary, and neural network is the best choice.[Limitations] We did not use the same dataset to evaluate all methods.[Conclusions] We proposed knowledge-boosted methods, transfer learning and cognitive models for future studies.

Key wordsDomain-Specific Event Graph    Knowledge Graph    Event Extraction    Information Extraction
收稿日期: 2020-05-05      出版日期: 2020-07-17
ZTFLH:  G350  
基金资助:*本文系国家社会科学基金重大项目“中国近现代文学期刊全文数据库建设与研究(1872-1949)”的研究成果之一(17ZDA276)
通讯作者: 成颖     E-mail: Chengy@nju.edu.cn
引用本文:   
王毅,沈喆,姚毅凡,成颖. 领域事件图谱构建方法综述*[J]. 数据分析与知识发现, 2020, 4(10): 1-13.
Wang Yi,Shen Zhe,Yao Yifan,Cheng Ying. Domain-Specific Event Graph Construction Methods:A Review. Data Analysis and Knowledge Discovery, 2020, 4(10): 1-13.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2020.0383      或      http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2020/V4/I10/1
Fig.1  领域事件图谱构建流程
Fig.2  事件图谱层次关系
Fig.3  事件抽取例子
方法分类 方法 学习方式 事件触发词识别 事件要素识别
P R F1 P R F1
基于特征的方法 Li’s baseline[61] 联合学习 74.5% 59.1% 65.9% 74.1% 37.4% 49.7%
基于特征的方法 Liao’s cross-event[43] 管道模型 68.7% 68.9% 68.8% 50.9% 49.7% 50.3%
基于特征的方法 Hong’s cross-entity[42] 管道模型 72.9% 64.3% 68.3% 53.4% 52.9% 53.1%
基于神经网络的方法 CNN[46] 管道模型 72.5% 43.1% 66.3% 51.6% 36.6% 48.9%
基于神经网络的方法 DMCNN[46] 管道模型 74.6% 50.9% 69.1% 54.6% 48.7% 53.5%
基于神经网络的方法 JRNN[52] 联合模型 66.0% 73.0% 69.3% 61.4% 64.2% 62.8%
Table 1  事件抽取代表模型在ACE数据集的效果对比
方法 算法 关系类型 R P F1
KERNEL-method[55] Voted Perceptron Person-Affiliation 81.62% 90.05% 85.61%
KERNEL-method[55] SVM Person-Affiliation 82.73% 91.32% 86.80%
Joint Extraction[57] Hybrid Neural Network Live_In/Work_For 78.30% 83.20% 80.60%
Event Co-occurrence Network[60] Co-occurrence Network Casual/Follow Accompany/Taxonomic 85.20% 89.60% 87.30%
Event-Argument Relation Extraction[54] SVM Event-Argument - - 69.70%
Long-Distance[59] Linear+PTK Time-Event 64.00% 60.90% 62.30%
Temporal Relation Extraction[56] CNN Time-Event 68.10% 72.70% 70.30%
Temporal Relation Extraction[56] LSTM Time-Event 66.00% 69.80% 67.90%
Joint Reasoning[58] Joint Learning Temporal/Causal 74.40% 69.30% 71.80%
Table 2  事件关系抽取效果对比
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