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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (10): 1-13    DOI: 10.11925/infotech.2096-3467.2020.0383
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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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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 words： Domain-Specific Event Graph      Knowledge Graph      Event Extraction      Information Extraction     
Received: 05 May 2020      Published: 17 July 2020
ZTFLH:  G350  
Corresponding Authors: Cheng Ying     E-mail: Chengy@nju.edu.cn
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Cite this article:

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.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2020.0383     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2020/V4/I10/1

Domain-Specific Graph Construction Process
Event Graph Relations
Example of Event Extraction
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%
Performance of Event Extraction Model in ACE Dataset
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%
Event Relation Extraction Performance
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