[1] |
COWIE J , LEHNERT W . Information extraction[J]. Communications of the ACM, 1996,39(1): 80-91.
|
[2] |
AMIT S . Introducing the knowledge graph[R]. America:Official Blog of Google, 2012.
|
[3] |
邹艳珍, 王敏, 谢冰 ,等. 基于大数据的软件项目知识图谱构造及问答方法[J]. 大数据, 2021,7(1): 22-36.
|
|
ZOU Y Z , WANG M , XIE B ,et al. Software knowledge graph construction and Q & A technology based on big data[J]. Big Data Research, 2021,7(1): 22-36.
|
[4] |
陈成, 陈跃国, 刘宸 ,等. 意图知识图谱的构建与应用[J]. 大数据, 2020,6(2): 57-68.
|
|
CHEN C , CHEN Y G , LIU C ,et al. Constructing and analyzing intention knowledge graphs[J]. Big Data Research, 2020,6(2): 57-68.
|
[5] |
孙镇, 王惠临 . 命名实体识别研究进展综述[J]. 现代图书情报技术, 2010(6): 42-47.
|
|
SUN Z , WANG H L . Overview on the advance of the research on named entity recognition[J]. New Technology of Library and Information Service, 2010(6): 42-47.
|
[6] |
ETZIONI O , BANKO M , SODERLAND S ,et al. Open information extraction from the Web[J]. Communications of the ACM, 2008,51(12): 68-47.
|
[7] |
BERNERS-LEE T , HENDLER J , LASSILA O . The semantic Web[J]. Scientific American, 2001,284(5): 34-43.
|
[8] |
赵军, 刘康, 何世柱 . 知识图谱[M]. 北京: 高等教育出版社, 2018.
|
|
ZHAO J , LIU K , HE S Z . Knowledge graph[M]. Beijing: Higher Education Press, 2018.
|
[9] |
项威 . 事件知识图谱构建技术与应用综述[J]. 计算机与现代化, 2020(1): 10-16.
|
|
XIANG W . Reviews on event knowledge graph construction techniques and application[J]. Computer and Modernization, 2020(1): 10-16.
|
[10] |
ROSPOCHER M , VAN ERP M , VOSSEN P ,et al. Building event-centric knowledge graphs from news[J]. Journal of Web Semantics, 2016,37: 132-151.
|
[11] |
LIU S , CHEN Y , LIU K ,et al. Exploiting argument information to improve event detection via supervised attention mechanisms[C]// Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Stroudsburg:ACL Press, 2017: 1789-1798.
|
[12] |
CHEN Y , YANG H , LIU K ,et al. Collective event detection via a hierarchical and bias tagging networks with gated multilevel attention mechanisms[C]// Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Stroudsburg:ACL Press, 2018: 1267-1276.
|
[13] |
YANG S , FENG D , QIAO L ,et al. Exploring pre-trained language models for event extraction and generation[C]// Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Stroudsburg:ACL Press, 2019: 5284-5294.
|
[14] |
WU Z , PAN S , CHEN F ,et al. A comprehensive survey on graph neural networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021,32(1): 4-24.
|
[15] |
NGUYEN T , GRISHMAN R . Graph convolutional networks with argumentaware pooling for event detection[C]// Proceedings of the AAAI Conference on Artificial Intelligence. Menlo Park:AAAI Press, 2018.
|
[16] |
LIU X , LUO Z C , HUANG H Y . Jointly multiple events extraction via attentionbased graph information aggregation[C]// Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Stroudsburg:ACL Press, 2018: 1247-1256.
|
[17] |
LIU J , CHEN Y B , LIU K . Exploiting the ground-truth:an adversarial imitation based knowledge distillation approach for event detection[C]// Proceedings of the AAAI Conference on Artificial Intelligence. Menlo Park:AAAI Press, 2019: 6754-6761.
|
[18] |
HONG Y , ZHOU W X , ZHANG J L ,et al. Self-regulation:employing a generative adversarial network to improve event detection[C]// Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. Stroudsburg:ACL Press, 2018: 515-526.
|
[19] |
ARAKI J , MITAMURA T . Opendomain event detection using distant supervision[C]// Proceedings of the 27th International Conference on Computational Linguistics. New York:ACM Press, 2018: 878-891.
|
[20] |
MILLER G A , BECKWITH R , FELLBAUM C ,et al. Introduction to WordNet:an on-line lexical database[J]. International journal of Lexicography, 1990,3(4): 235-244.
|
[21] |
WANG R , ZHOU D Y , HE Y L . Open event extraction from online text using a generative adversarial network[C]// Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Stroudsburg:ACL Press, 2019: 282-291.
|
[22] |
NAIK A , ROSE C . Towards open domain event trigger identification using adversarial domain adaptation[C]// Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg:ACL Press, 2020: 7618-7624.
|
[23] |
BOLLACKER K , EVANS C , PARITOSH P ,et al. Freebase:a collaboratively created graph database for structuring human knowledge[C]// Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data. New York:ACM Press, 2008: 1247-1250.
|
[24] |
WEN J , LI J , MAO Y ,et al. On the representation and embedding of knowledge bases beyond binary relations[C]// Proceedings of the 25th International Joint Conference on Artificial Intelligence. San Francisco:Morgan Kaufmann, 2016: 1300-1307.
|
[25] |
ZHANG R C , LI J P , MEI J J ,et al. Scalable instance reconstruction in knowledge bases via relatedness affiliated embedding[C]// Proceedings of the 2018 World Wide Web Conference. New York:ACM Press, 2018: 1185-1194.
|
[26] |
GUAN S P , JIN X L , WANG Y Z ,et al. Link prediction on N-ary relational data[C]// Proceedings of the World Wide Web Conference. New York:ACM Press, 2019: 583-593.
|
[27] |
GUAN S P , JIN X L , GUO J F ,et al. NeuInfer:knowledge inference on N-ary facts[C]// Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg:ACL Press, 2020: 6141-6151.
|
[28] |
ZENG Y T , JIN X L , GUAN S P ,et al. Event coreference resolution with their paraphrases and argumentaware embeddings[C]// Proceedings of the 28th International Conference on Computational Linguistics. Stroudsburg:ACL Press, 2020: 3084-3094.
|
[29] |
CHENG F , MIYAO Y . Classifying temporal relations by bidirectional LSTM over dependency paths[C]// Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Stroudsburg:ACL Press, 2017: 1-6.
|
[30] |
HAN R J , NING Q , PENG N Y . Joint event and temporal relation extraction with shared representations and structured prediction[C]// Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Stroudsburg:ACL Press, 2019: 434-444.
|
[31] |
HAN R J , HSU I H , YANG M ,et al. Deep structured neural network for event temporal relation extraction[C]// Proceedings of the 23rd Conference on Computational Natural Language Learning. Stroudsburg:ACL Press, 2019.
|
[32] |
HAN R J , ZHOU Y C , PENG N Y . Domain knowledge empowered structured neural net for end-to-end event temporal relation extraction[C]// Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. Stroudsburg:ACL Press, 2020: 5717-5729.
|
[33] |
WANG H Y , CHEN M H , ZHANG H M ,et al. Joint constrained learning for eventevent relation extraction[C]// Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. Stroudsburg:ACL Press, 2020: 696-706.
|
[34] |
CASELLI T , VOSSEN P . The event StoryLine corpus:a new benchmark for causal and temporal relation extraction[C]// Proceedings of the Events and Stories in the News Workshop. Stroudsburg:ACL Press, 2017: 77-86.
|
[35] |
GAO L , CHOUBEY P K , HUANG R H . Modeling document-level causal structures for event causal relation identification[C]// Proceedings of the 2019 Conference of the North. Stroudsburg:ACL Press, 2019: 1808-1817.
|
[36] |
LIU J , CHEN Y B , ZHAO J . Knowledge enhanced event causality identification with mention masking generalizations[C]// Proceedings of the 29th International Joint Conference on Artificial Intelligence. San Francisco:Morgan Kaufmann, 2020: 3608-3614.
|
[37] |
LIU H , SINGH P . ConceptNet—a practical commonsense reasoning tool-kit[J]. BT Technology Journal, 2004,22(4): 211-226.
|
[38] |
CHAMBERS N , JURAFSKY D . Unsupervised learning of narrative event chains[C]// Proceedings of ACL-08:HLT. Stroudsburg:ACL Press, 2008: 789-797.
|
[39] |
GRANROTH-WILDING M , CLARK S . What happens next? Event prediction using a compositional neural network model[C]// Proceedings of the AAAI Conference on Artificial Intelligence. Menlo Park:AAAI Press, 2016.
|
[40] |
LI B , LEE-URBAN S , JOHNSTON G ,et al. Story generation with crowdsourced plot graphs[C]// Proceedings of the 27th AAAI Conference on Artificial Intelligence. Menlo Park:AAAI Press, 2013: 598-604.
|
[41] |
GLAVA? G , ?NAJDER J . Construction and evaluation of event graphs[J]. Natural Language Engineering, 2015,21(4): 607-652.
|
[42] |
LI Z Y , DING X , LIU T . Constructing narrative event evolutionary graph for script event prediction[C]// Proceedings of the 27th International Joint Conference on Artificial Intelligence. San Francisco:Morgan Kaufmann, 2018: 4201-4207.
|
[43] |
HAN Z , WANG Y Y , MA Y P ,et al. The graph Hawkes network for reasoning on temporal knowledge graphs[C]// Proceedings of the Automated Knowledge Base Construction.[S.l.:s.n.], 2020.
|
[44] |
HAWKES A G . Spectra of some self-exciting and mutually exciting point processes[J]. Biometrika, 1971,58(1): 83-90.
|
[45] |
JIN W , QU M , JIN X S ,et al. Recurrent event network:autoregressive structure inference over temporal knowledge graphs[C]// Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. Stroudsburg:ACL Press, 2020: 6669-6683.
|
[46] |
王瑞 . 网络舆情事件知识图谱构建技术及应用研究[D]. 泉州:华侨大学, 2020.
|
|
WANG R . Research on construction technology and application of knowledge graph for internet public opinion event[D]. Quanzhou:Huaqiao University, 2020.
|