  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.
