Learning Interpretable Relationships between Entities, Relations and Concepts via Bayesian Structure Learning on Open Domain Facts
Learning Interpretable Relationships between Entities, Relations and Concepts via Bayesian Structure Learning on Open Domain Facts
复制标题
通过开放领域事实的贝叶斯结构学习来学习实体、关系和概念之间的可解释关系
DOI：
10.18653/v1/2020.acl-main.717
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Ping Li
中科院分区：
文献类型：
--
作者：
Jingyuan Zhang;Mingming Sun;Yue Feng;Ping Li
概念图是开放领域知识中文本理解的通用分类法。概念图中的节点包括实体和概念。边是从实体到概念，表明实体是概念的实例。在本文中，我们提出了从开放域事实中学习可解释关系的任务，以丰富和细化概念图。贝叶斯网络结构是从开放域事实中学习的，作为事实关系和实体概念之间的可解释关系。我们在公共英语和汉语数据集上进行了大量的实验。与最先进的方法相比，学习的网络结构有助于基于两个数据集上实体的关系来改进实体的概念识别。
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.
通过生长 Chow-Liu 树网络进行拓扑识别
DOI：
10.1109/cdc.2018.8619207
发表时间：
2018
期刊：
2018 IEEE Conference on Decision and Control (CDC
影响因子：
--
作者：
Hassan-Moghaddam, Sepideh;Jovanovic, Mihailo R.
通讯作者：
Jovanovic, Mihailo R.