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• W3035306518 abstract "Concept graphs are created as universal taxonomies for text understanding in the open-domain knowledge. The nodes in concept graphs include both entities and concepts. The edges are from entities to concepts, showing that an entity is an instance of a concept. In this paper, we propose the task of learning interpretable relationships from open-domain facts to enrich and refine concept graphs. The Bayesian network structures are learned from open-domain facts as the interpretable relationships between relations of facts and concepts of entities. We conduct extensive experiments on public English and Chinese datasets. Compared to the state-of-the-art methods, the learned network structures help improving the identification of concepts for entities based on the relations of entities on both datasets." @default.
• W3035306518 created "2020-06-19" @default.
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• W3035306518 date "2020-01-01" @default.
• W3035306518 modified "2023-10-16" @default.
• W3035306518 title "Learning Interpretable Relationships between Entities, Relations and Concepts via Bayesian Structure Learning on Open Domain Facts" @default.
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• W3035306518 doi "https://doi.org/10.18653/v1/2020.acl-main.717" @default.
• W3035306518 hasPublicationYear "2020" @default.
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