Leave no Thought Behind: Encoding Context-rich KGs from Natural Language
May 10 | KGC 2023
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32m
Many industries store vast amounts of information as natural language. Current methods for composing this text into knowledge graphs parse a small set of relations from within a larger document. The author's specific diction is approximated by the vocabulary of the model. In domains where precise communication is critical, this approach is not sufficient. We propose a novel approach to encode natural language into a knowledge graph without any loss of context.
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