| Reference: | [1] Farhad Abedini, Mohammad Reza Keyvanpour, and Mohammad Bagher Menhaj.Correction tower: A general embedding method of the error recognition for the knowledgegraph correction. Int. J. Pattern Recognit. Artif. Intell., 34:2059034:1–2059034:38, 2020.
[2] Ivana Balažević, Carl Allen, and Timothy M Hospedales. Tucker: Tensor factorizationfor knowledge graph completion. In Empirical Methods in Natural Language Processing,2019.
[3] Anson Bastos, Abhishek Nadgeri, Kuldeep Singh, Isaiah Onando Mulang, SaeedehShekarpour, Johannes Hoffart, and Manohar Kaul. Recon: relation extraction usingknowledge graph context in a graph neural network. In Proceedings of the Web Conference2021, pages 1673–1685, 2021.
[4] Iz Beltagy, Matthew E Peters, and Arman Cohan. Longformer: The long-documenttransformer. arXiv preprint arXiv:2004.05150, 2020.
[5] Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre. Fastunfolding of communities in large networks. Journal of statistical mechanics: theory andexperiment, 2008(10):P10008, 2008.
[6] Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and OksanaYakhnenko. Translating embeddings for modeling multi-relational data. In C. J. C. Burges,L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger, editors, Advances in NeuralInformation Processing Systems, volume 26. Curran Associates, Inc., 2013.
[7] Mingming Chen, Konstantin Kuzmin, and Boleslaw K Szymanski. Community detectionvia maximization of modularity and its variants. IEEE Transactions on ComputationalSocial Systems, 1(1):46–65, 2014.
[8] Shib Sankar Dasgupta, Swayambhu Nath Ray, and Partha Talukdar. HyTE: Hyperplane-based temporally aware knowledge graph embedding. In Proceedings of the 2018Conference on Empirical Methods in Natural Language Processing, pages 2001–2011,Brussels, Belgium, October-November 2018. Association for Computational Linguistics.
[9] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: Pre-trainingof deep bidirectional transformers for language understanding. In Proceedings of the2019 Conference of the North American Chapter of the Association for ComputationalLinguistics: Human Language Technologies, Volume 1 (Long and Short Papers),pages 4171–4186, Minneapolis, Minnesota, June 2019. Association for ComputationalLinguistics.
[10] Wenfei Fan, Xueli Liu, Ping Lu, and Chao Tian. Catching numeric inconsistencies ingraphs. In Proceedings of the 2018 International Conference on Management of Data,SIGMOD ’18, page 381–393, New York, NY, USA, 2018. Association for ComputingMachinery.
[11] Alberto García-Durán, Sebastijan Dumancic, and Mathias Niepert. Learning sequenceencoders for temporal knowledge graph completion. In EMNLP, 2018.
[12] Alex Graves, Santiago Fernández, and Jürgen Schmidhuber. Bidirectional lstm networksfor improved phoneme classification and recognition. In Proceedings of the 15thInternational Conference on Artificial Neural Networks: Formal Models and TheirApplications - Volume Part II, ICANN’05, page 799–804, Berlin, Heidelberg, 2005.Springer-Verlag.
[13] Zhijiang Guo, Yan Zhang, and Wei Lu. Attention guided graph convolutional networksfor relation extraction. arXiv preprint arXiv:1906.07510, 2019.
[14] Iris Hendrickx, Su Nam Kim, Zornitsa Kozareva, Preslav Nakov, Diarmuid Ó Séaghdha,Sebastian Padó, Marco Pennacchiotti, Lorenza Romano, and Stan Szpakowicz. SemEval-2010 task 8: Multi-way classification of semantic relations between pairs of nominals.In Proceedings of the 5th International Workshop on Semantic Evaluation, pages 33–38,Uppsala, Sweden, July 2010. Association for Computational Linguistics.
[15] Tingsong Jiang, Tianyu Liu, Tao Ge, Lei Sha, Baobao Chang, Sujian Li, and Zhifang Sui.Towards time-aware knowledge graph completion. In Proceedings of COLING 2016, the26th International Conference on Computational Linguistics: Technical Papers, pages1715–1724, Osaka, Japan, December 2016. The COLING 2016 Organizing Committee.
[16] Woojeong Jin, Meng Qu, Xisen Jin, and Xiang Ren. Recurrent event network:Autoregressive structure inference over temporal knowledge graphs. In EMNLP, 2020.
[17] Jaehun Jung, Jinhong Jung, and U Kang. T-gap: Learning to walk across time for temporalknowledge graph completion. arXiv preprint arXiv:2012.10595, 2020.
[18] Jaehun Jung, Jinhong Jung, and U Kang. Learning to walk across time for interpretabletemporal knowledge graph completion. In Proceedings of the 27th ACM SIGKDDConference on Knowledge Discovery amp; Data Mining, KDD ’21, page 786–795, NewYork, NY, USA, 2021. Association for Computing Machinery.
[19] Timothée Lacroix, Guillaume Obozinski, and Nicolas Usunier. Tensor decompositions fortemporal knowledge base completion. arXiv preprint arXiv:2004.04926, 2020.
[20] Huiying Li, Yuanyuan Li, Feifei Xu, and Xinyu Zhong. Probabilistic error detecting innumerical linked data. In Proceedings, Part I, of the 26th International Conference onDatabase and Expert Systems Applications - Volume 9261, DEXA 2015, page 61–75,Berlin, Heidelberg, 2015. Springer-Verlag.
[21] Chin-Yew Lin and Franz Josef Och. Automatic evaluation of machine translation qualityusing longest common subsequence and skip-bigram statistics. In Proceedings of the 42ndAnnual Meeting of the Association for Computational Linguistics (ACL-04), pages 605–612, Barcelona, Spain, July 2004.
[22] Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy,Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. Roberta: A robustly optimized bertpretraining approach. ArXiv, abs/1907.11692, 2019.
[23] Yunpu Ma, Volker Tresp, and Erik A. Daxberger. Embedding models for episodicknowledge graphs. J. Web Semant., 59, 2019.
[24] Mike Mintz, Steven Bills, Rion Snow, and Dan Jurafsky. Distant supervision for relationextraction without labeled data. In Proceedings of the Joint Conference of the 47th AnnualMeeting of the ACL and the 4th International Joint Conference on Natural LanguageProcessing of the AFNLP, pages 1003–1011, 2009.
[25] Paramita Mirza and Sara Tonelli. Catena: Causal and temporal relation extraction fromnatural language texts. In The 26th international conference on computational linguistics,pages 64–75. ACL, 2016.
[26] Abhishek Nadgeri, Anson Bastos, Kuldeep Singh, Isaiah Onando Mulang, JohannesHoffart, Saeedeh Shekarpour, and Vijay Saraswat. Kgpool: Dynamic knowledge graphcontext selection for relation extraction. arXiv preprint arXiv:2106.00459, 2021.
[27] Heiko Paulheim. Identifying wrong links between datasets by multi-dimensional outlierdetection. In WoDOOM, pages 27–38, 2014.
[28] Heiko Paulheim. Knowledge graph refinement: A survey of approaches and evaluationmethods. Semantic web, 8(3):489–508, 2017.
[29] Julia Perl, Claudia Wagner, Jerome Kunegis, and Steffen Staab. Twitter as a politicalnetwork: Predicting the following and unfollowing behavior of german politicians. InProceedings of the ACM Web Science Conference, pages 1–2, 2015.
[30] Chris Quirk and Hoifung Poon. Distant supervision for relation extraction beyond thesentence boundary. arXiv preprint arXiv:1609.04873, 2016.
[31] Sebastian Riedel, Limin Yao, and Andrew McCallum. Modeling relations and theirmentions without labeled text. In José Luis Balcázar, Francesco Bonchi, Aristides Gionis,and Michèle Sebag, editors, Machine Learning and Knowledge Discovery in Databases,pages 148–163, Berlin, Heidelberg, 2010. Springer Berlin Heidelberg.
[32] Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, andMichael Bronstein. Temporal graph networks for deep learning on dynamic graphs, 2020.
[33] Aravind Sankar, Yanhong Wu, Liang Gou, Wei Zhang, and Hao Yang. Dysat: Deep neuralrepresentation learning on dynamic graphs via self-attention networks. In Proceedings ofthe 13th International Conference on Web Search and Data Mining, pages 519–527, 2020.
[34] Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, andMax Welling. Modeling relational data with graph convolutional networks. In EuropeanSemantic Web Conference, pages 593–607. Springer, 2018.
[35] Livio Baldini Soares, Nicholas FitzGerald, Jeffrey Ling, and Tom Kwiatkowski.Matching the blanks: Distributional similarity for relation learning. arXiv preprintarXiv:1906.03158, 2019.
[36] Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, andYoshua Bengio. Graph attention networks. stat, 1050:20, 2017.
[37] Hong Wang, Christfried Focke, Rob Sylvester, Nilesh Mishra, and William Wang. Fine-tune bert for docred with two-step process, 2019.
[38] ZhongXian Wang, XiangHui He, and XingYan Hu. Chinese sentence compressionalgorithm based on deep analysis of sentence hierarchy in multiple application scenarios.In 2020 3rd International Conference on Advanced Electronic Materials, Computers andSoftware Engineering (AEMCSE), pages 61–66, 2020.
[39] Shanchan Wu and Yifan He. Enriching pre-trained language model with entity informationfor relation classification. In Proceedings of the 28th ACM International Conference onInformation and Knowledge Management, CIKM ’19, page 2361–2364, New York, NY,USA, 2019. Association for Computing Machinery.
[40] Wei Xu and Ralph Grishman. A parse-and-trim approach with information significancefor Chinese sentence compression. In Proceedings of the 2009 Workshop on LanguageGeneration and Summarisation (UCNLG+Sum 2009), pages 48–55, Suntec, Singapore,August 2009. Association for Computational Linguistics.
[41] Jianhao Yan, Lin He, Ruqin Huang, Jian Li, and Ying Liu. Relation extraction withtemporal reasoning based on memory augmented distant supervision. In Proceedings ofthe 2019 Conference of the North American Chapter of the Association for ComputationalLinguistics: Human Language Technologies, Volume 1 (Long and Short Papers),pages 1019–1030, Minneapolis, Minnesota, June 2019. Association for ComputationalLinguistics.
[42] Jianhao Yan, Lin He, Ruqin Huang, Jian Li, and Ying Liu. Relation extraction withtemporal reasoning based on memory augmented distant supervision. In Proceedings ofthe 2019 Conference of the North American Chapter of the Association for ComputationalLinguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages1019–1030, 2019.
[43] Bishan Yang, Wen tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. Embedding entitiesand relations for learning and inference in knowledge bases. CoRR, abs/1412.6575, 2015.
[44] Daojian Zeng, Kang Liu, Yubo Chen, and Jun Zhao. Distant supervision for relationextraction via piecewise convolutional neural networks. In Proceedings of the 2015conference on empirical methods in natural language processing, pages 1753–1762, 2015.
[45] Daojian Zeng, Kang Liu, Siwei Lai, Guangyou Zhou, and Jun Zhao. Relationclassification via convolutional deep neural network. In Proceedings of COLING 2014,the 25th International Conference on Computational Linguistics: Technical Papers, pages2335–2344, Dublin, Ireland, August 2014. Dublin City University and Association forComputational Linguistics.
[46] Ben Zhou, Kyle Richardson, Qiang Ning, Tushar Khot, Ashish Sabharwal, and DanRoth. Temporal reasoning on implicit events from distant supervision. arXiv preprintarXiv:2010.12753, 2020.
[47] Cunchao Zhu, Muhao Chen, Changjun Fan, Guangquan Cheng, and Yan Zhan. Learningfrom history: Modeling temporal knowledge graphs with sequential copy-generationnetworks. arXiv preprint arXiv:2012.08492, 2020.
[48] Kangli Zi, Shi Wang, Yu Liu, Jicun Li, Yanan Cao, and Cungen Cao. SOM-NCSCM :An efficient neural Chinese sentence compression model enhanced with self-organizingmap. In Proceedings of the 2021 Conference on Empirical Methods in Natural LanguageProcessing, pages 403–415, Online and Punta Cana, Dominican Republic, November2021. Association for Computational Linguistics. |
