DEER: Descriptive Knowledge Graph for Explaining Entity Relationships

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 highquality relation descriptions for explaining entity relationships. The results suggest that we can build an open and informative knowledge graph without human annotation. 1 Artificial Intelligence Computer Science Deep Learning Machine Learning Algorithm As of 2020, deep learning has become the dominant approach for much ongoing work in the field of machine learning. Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Machine learning is a subfield of soft computing within computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. As a scientific endeavor, machine learning grew out of the quest for artificial intelligence. Regularization Regularization, in the context of machine learning, refers to the process of modifying a learning algorithm so as to prevent overfitting.