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In the current digitalization era, capturing and effectively representing knowledge is crucial in most real-world scenarios.
Machine knowledge: Creation and curation of comprehensive knowledge bases
G. Weikum, X. L. Dong, S. Razniewski, and F. Suchanek · 1931
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Binary Codes Capable of Correcting Deletions, Insertions and Reversals
V. Levenshtein · 1966
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Relation extraction: Perspective from convolutional neural networks
T. H. Nguyen and R. Grishman · 2015
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Named Entity Recognition with Bidirectional LSTM-CNNs
J. P. Chiu and E. Nichols · 2016
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Deeper - deep entity resolution
M. Ebraheem, S. Thirumuruganathan, S. R. Joty, M. Ouzzani, and N. Tang · 2017
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D. Cer, Y. Yang, S. yi Kong, N. Hua, N. L. U. Limtiaco, R. S. John, N. Constant, M. Guajardo-Céspedes, S. Yuan, C. Tar, Y. hsuan Sung, B. Strope, and R. Kurzweil · 2018
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Y. Luan, L. He, M. Ostendorf, and H. Hajishirzi · 2018
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A survey on open information extraction
C. Niklaus, M. Cetto, A. Freitas, and S. Handschuh · 2018
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Scalable knowledge graph construction over text using deep learning based predicate mapping
A. Mehta, A. Singhal, and K. Karlapalem · 2019
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A high precision pipeline for financial knowledge graph construction
S. Elhammadi, L. V. Lakshmanan, R. Ng, M. Simpson, B. Huai, Z. Wang, and L. Wang · 2020
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Language models are open knowledge graphs
C. Wang, X. Liu, and D. Song · 2020
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Semantic service search in it crowdsourcing platform: A knowledge graph-based approach
Q. Wu, D. Fu, B. Shen, and Y. Chen · 2020
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Machine learning: Algorithms, real-world applications and research directions
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Large language models are few-shot clinical information extractors
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L. Bonifacio, H. Abonizio, M. Fadaee, and R. Nogueira · 2022
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Bertnet: Harvesting knowledge graphs from pretrained language models
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An efficient joint framework for interacting knowledge graph and item recommendation
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Enhancing knowledge graph construction using large language models
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