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The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

ERLKG: Entity Representation Learning and Knowledge Graph based association analysis of COVID-19 through mining of unstructured biomedical corpora

Basu, Sayantan, Chakraborty, Sinchani, Hassan, Atif, Siddique, Sana, Anand, Ashish·2020·Proceedings of the First Workshop on Scholarly Document Processing
DOI 10.18653/v1/2020.sdp-1.15
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  • Manual evaluation via domain experts and annotators is a quality metric that usually predicts accuracy with the agreement of human annotators.

    “Manual evaluation via domain experts and annotators is the quality metric that usually predict accuracy with the agreement of the human annotators.”

    From Scholarly knowledge graphs through structuring scholarly communication: a review (2023) · cites this paper as [59]

    Section 4.5 (page not provided) describes ratings by two physicians and reports their inter-rater agreement; Section 5.3 compares physician ratings with model scores. The paper supports expert evaluation and agreement as parts of its evaluation, but does not say that annotator agreement usually predicts accuracy or is the quality metric. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • In [59], two subsets from the dataset were created to provide ratings by physicians to analyze relatedness of entities and to finalize the embedding method.

    “In [59], two subset from data set are created to provide ratings by physicians to analyze relatedness of entities and to finalize embedding method.”

    From Scholarly knowledge graphs through structuring scholarly communication: a review (2023) · cites this paper as [59]

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Research works printed but not linked: 31

  1. Abdullah S Al Saleh, Taimur Sher, and Morie A Gertz. 2020. Multiple myeloma in the time of covid-19. Acta haematologica, pages 1-7.
  2. Emily Alsentzer, John R. Murphy, Willie Boag, Wei- Hung Weng, Di Jin, Tristan Naumann, and Matthew B. A. McDermott. 2019. Publicly available clinical bert embeddings. ArXiv, abs/1904.03323. · arXiv
  3. Antoine Bordes, Nicolas Usunier, Alberto García- Durán, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi- relational data. In NIPS.
  4. Berend Jan Bosch, Ruurd van der Zee, Cornelis A M de Haan, and Peter J. M. Rottier. 2003. The coron- avirus spike protein is a class i virus fusion protein: Structural and functional characterization of the fu- sion core complex. Journal of Virology, 77:8801 - 8811.
  5. Bin Cao, Yeming Wang, Danning Wen, Wen Liu, Jingli Wang, Guohui Fan, Lianguo Ruan, Bin Song, Yan- ping Cai, Ming Wei, et al. 2020. A trial of lopinavir- ritonavir in adults hospitalized with severe covid-19. New England Journal of Medicine.
  6. Chongyan Chen, Islam Akef Ebeid, Yi Bu, and Ying Ding. 2020. Coronavirus knowledge graph: A case study. ArXiv, abs/2007.10287. · arXiv
  7. Fadi Delly, Maryam Jamil Syed, Robert P. Lisak, and Deepti Zutshi. 2020. Myasthenic crisis in covid-19. Journal of the Neurological Sciences, 414:116888 - 116888.
  8. Tim Dettmers, Pasquale Minervini, Pontus Stene- torp, and Sebastian Riedel. 2018. Convolu- tional 2d knowledge graph embeddings. ArXiv, abs/1707.01476. · arXiv
  9. Rezarta Islamaj Dogan, Robert Leaman, and Zhiyong Lu. 2014. Ncbi disease corpus: A resource for dis- ease name recognition and concept normalization. Journal of biomedical informatics, 47:1-10.
  10. Daniel Domingo-Fernández, Shounak Baksi, Bruce Schultz, Yojana Gadiya, Reagon Karki, Tamara Raschka, Christian Ebeling, Martin Hofmann- Apitius, and Alpha Tom Kodamullil. 2020. Covid- 19 knowledge graph: a computable, multi-modal, cause-and-effect knowledge model of covid-19 pathophysiology. bioRxiv.
  11. Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
  12. Xu Han, Shulin Cao, Lv Xin, Yankai Lin, Zhiyuan Liu, Maosong Sun, and Juanzi Li. 2018. Openke: An open toolkit for knowledge embedding. In Proceed- ings of EMNLP.
  13. Yi Han, Haidong Zhang, Sucheng Mu, Wei Wei, Chaoyuan Jin, Yuan Xue, Chaoyang Tong, Yunfei Zha, Zhenju Song, and Guorong Gu. 2020. Lactate dehydrogenase, a risk factor of severe covid-19 pa- tients. medRxiv.
  14. Mahalaxmi Iyer, Kaavya Jayaramayya, Mohana Devi Subramaniam, Soo Bin Lee, Ahmed Abdal Dayem, Ssang-Goo Cho, and Balachandar Vellingiri. 2020. Covid-19: an update on diagnostic and therapeutic approaches. BMB reports, 53(4):191.
  15. Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, and Jun Zhao. 2015. Knowledge graph embedding via dy- namic mapping matrix. In ACL.
  16. Thomas Kipf and Max Welling. 2016. Variational graph auto-encoders. ArXiv, abs/1611.07308. · arXiv
  17. Martin Krallinger, Obdulia Rabal, Florian Leit- ner, Miguel Vazquez, David Salgado, Zhiyong Lu, Robert Leaman, Yanan Lu, Dong-Hong Ji, Daniel M. Lowe, Roger A. Sayle, Riza Theresa Batista-Navarro, Rafal Rak, Torsten Huber, Tim Rocktäschel, Sérgio Matos, David Campos, Buzhou Tang, Hua Xu, Tsendsuren Munkhdalai, Keun Ho Ryu, S. V. Ramanan, P. Senthil Nathan, Slavko Zitnik, Marko Bajec, Lutz Weber, Matthias Irmer, Saber A. Akhondi, Jan A. Kors, Shuo Xu, Xin An, Utpal Kumar Sikdar, Asif Ekbal, Masaharu Yosh- ioka, Thaer M. Dieb, Miji Choi, Karin M. Verspoor, Madian Khabsa, C. Lee Giles, Hongfang Liu, K. E. Ravikumar, Andre Lamurias, Francisco M. Couto, Hong-Jie Dai, Richard Tzong-Han Tsai, Caglar Ata, Tolga Can, Anabel Usie, Rui Alves, Isabel Segura- Bedmar, Paloma Martínez, Julen Oyarzábal, and Alfonso Valencia. 2015. The chemdner corpus of chemicals and drugs and its annotation principles. Journal of Cheminformatics, 7:S2 -S2.
  18. Jens Kringelum, Sonny Kim Kjærulff, Søren Brunak, Ole Lund, Tudor I. Oprea, and Olivier Taboureau. 2016. Chemprot-3.0: a global chemical biology dis- eases mapping. Database: The Journal of Biologi- cal Databases and Curation, 2016.
  19. Chih-Cheng Lai, Tzu-Ping Shih, Wen-Chien Ko, Hung- Jen Tang, and Po-Ren Hsueh. 2020. Severe acute respiratory syndrome coronavirus 2 (sars-cov-2) and corona virus disease-2019 (covid-19): the epidemic and the challenges. International journal of antimi- crobial agents, page 105924.
  20. Jiao Li, Yueping Sun, Robin J. Johnson, Daniela Sci- aky, Chih-Hsuan Wei, Robert Leaman, Allan Peter Davis, Carolyn J. Mattingly, Thomas C. Wiegers, and Zhiyong Lu. 2016. Biocreative v cdr task cor- pus: a resource for chemical disease relation ex- traction. Database: The Journal of Biological Databases and Curation, 2016.
  21. Edward Loper and Steven Bird. 2002. Nltk: the natural language toolkit. arXiv preprint cs/0205028. · arXiv
  22. Dai Quoc Nguyen, Thanh Vu, T. Nguyen, Dat Quoc Nguyen, and Dinh Q. Phung. 2019. A capsule network-based embedding model for knowledge graph completion and search personalization. ArXiv, abs/1808.04122. · arXiv
  23. Jong Won Park. 2020. Continual bert: Continual learn- ing for adaptive extractive summarization of covid- 19 literature. ArXiv, abs/2007.03405. · arXiv
  24. Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word repre- sentations. ArXiv, abs/1802.05365. · arXiv
  25. Kirk Roberts, Tasmeer Alam, Steven Bedrick, Dina Demner-Fushman, Kyle Lo, Ian Soboroff, Ellen M. Voorhees, Lucy Lu Wang, and William R. Hersh. 2020. Trec-covid: Rationale and structure of an in- formation retrieval shared task for covid-19. Journal of the American Medical Informatics Association : JAMIA.
  26. Matthias Thoms, Robert Buschauer, Michael Ameis- meier, Lennart Koepke, Timo Denk, Maximil- ian Hirschenberger, H. Kratzat, Manuel Hayn, T. Mackens-Kiani, Jingdong Cheng, C. Stürzel, T. Fröhlich, O. Berninghausen, T. Becker, F. Kirch- hoff, K. Sparrer, and R. Beckmann. 2020. Struc- tural basis for translational shutdown and immune evasion by the nsp1 protein of sars-cov-2. bioRxiv.
  27. Lucy Lu Wang, Kyle Lo, Yoganand Chandrasekhar, Russell Reas, Jiangjiang Yang, Darrin Eide, Kathryn Funk, Rodney Michael Kinney, Ziyang Liu, William. Merrill, Paul Mooney, Dewey A. Murdick, Devvret Rishi, Jerry Sheehan, Zhihong Shen, Brandon Stil- son, Alex D. Wade, Kuansan Wang, Christopher Wil- helm, Boya Xie, Douglas M. Raymond, Daniel S. Weld, Oren Etzioni, and Sebastian Kohlmeier. 2020a. Cord-19: The covid-19 open research dataset. ArXiv.
  28. Qingyun Wang, Manling Li, X. Wang, Nikolaus Nova Parulian, Guangxing Han, Jiawei Ma, Jingxuan Tu, Ying Lin, H. Zhang, Weili Liu, Aabhas Chauhan, Yingjun Guan, Bangzheng Li, Ruisong Li, Xi- angchen Song, Huai zhong Ji, Jiawei Han, Shih- Fu Chang, J. Pustejovsky, D. Liem, A. El-Sayed, Martha Palmer, Jasmine Rah, C. Schneider, and B. Onyshkevych. 2020b. Covid-19 literature knowl- edge graph construction and drug repurposing report generation. ArXiv, abs/2007.00576. · arXiv
  29. Xuan Wang, Xiangchen Song, Yingjun Guan, Bangzheng Li, and Jiawei Han. 2020c. Compre- hensive named entity recognition on cord-19 with distant or weak supervision. ArXiv, abs/2003.12218. · arXiv
  30. Bishan Yang, Wen tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. 2015. Embedding entities and relations for learning and inference in knowledge bases. CoRR, abs/1412.6575. · arXiv
  31. Z. Yang, Zihang Dai, Yiming Yang, J. Carbonell, R. Salakhutdinov, and Quoc V. Le. 2019. Xlnet: Generalized autoregressive pretraining for language understanding. In NeurIPS.

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Metadata

Authors
Basu, S., Chakraborty, S., Hassan, A., Siddique, S., & Anand, A.
Venue
Proceedings of the First Workshop on Scholarly Document Processing
Publisher
Association for Computational Linguistics
Published
2020
Pages
127-137
DOI
10.18653/v1/2020.sdp-1.15
Cited by
8
References
7

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