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.
翻译:我们提出“解释实体关系的描述性知识图”——一种开放和资料丰富的实体关系模式。在“设计性知识图”中,实体之间的关系以自由文本关系描述为代表。例如,机器学习实体与算法实体之间的关系可以表现为“机器学习和算法实体探索从数据中学习和作出预测的算法的研究和构建。”为了构建“设计DEER,我们建议一种自我监督的学习方法,以从分析依赖模式中提取关系描述,并用基于变压器的关系描述综合模型生成关系描述,其中不需要人类标签。实验表明,我们的系统可以提取和生成高质量的关系描述来解释实体关系。结果表明,我们可以在没有人类注解的情况下制作一个开放的、内容丰富的知识图表。