@inproceedings{zhang-etal-2020-learning-interpretable,
    title = "Learning Interpretable Relationships between Entities, Relations and Concepts via {B}ayesian Structure Learning on Open Domain Facts",
    author = "Zhang, Jingyuan  and
      Sun, Mingming  and
      Feng, Yue  and
      Li, Ping",
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.acl-main.717/",
    doi = "10.18653/v1/2020.acl-main.717",
    pages = "8045--8056",
    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."
}