论文标题
鹿：用于解释实体关系的描述性知识图
DEER: Descriptive Knowledge Graph for Explaining Entity Relationships
论文作者
论文摘要
我们提出了鹿（用于解释实体关系的描述性知识图） - 建模实体关系的开放且内容丰富的形式。在鹿中，实体之间的关系由自由文本的关系描述表示。 For instance, the relationship between entities of machine learning and algorithm can be represented as ``Machine learning explores the study and construction of algorithms that can learn from and make predictions on data.'' To construct DEER, we propose a self-supervised learning method to extract relation descriptions with the analysis of dependency patterns and generate relation descriptions with a transformer-based relation description synthesizing model, where no human labeling is required.实验表明，我们的系统可以提取并生成高质量的关系描述，以解释实体关系。结果表明，我们可以在没有人类注释的情况下建立开放且内容丰富的知识图。
We propose DEER (Descriptive Knowledge Graph for Explaining Entity Relationships) - an open and informative form of modeling entity relationships. In DEER, relationships between entities are represented by free-text relation descriptions. For instance, the relationship between entities of machine learning and algorithm can be represented as ``Machine learning explores the study and construction of algorithms that can learn from and make predictions on data.'' To construct DEER, we propose a self-supervised learning method to extract relation descriptions with the analysis of dependency patterns and generate relation descriptions with a transformer-based relation description synthesizing model, where no human labeling is required. Experiments demonstrate that our system can extract and generate high-quality relation descriptions for explaining entity relationships. The results suggest that we can build an open and informative knowledge graph without human annotation.
