Learning Interpretable Relationships between Entities, Relations and Concepts via Bayesian Structure Learning on Open Domain Facts (2020.acl-main)
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| Challenge: | Concept graphs are created as universal taxonomies for text understanding in the open domain knowledge. |
| Approach: | They propose to learn interpretable relationships from open-domain facts to enrich concept graphs. |
| Outcome: | The proposed method improves the identification of concepts for entities based on relations between entities on public English and Chinese datasets. |
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