DEER: Descriptive Knowledge Graph for Explaining Entity Relationships
This provides a method for building informative knowledge graphs without human labeling, which is useful for AI and NLP applications, though it is incremental as it builds on existing knowledge graph and text generation techniques.
The paper tackles the problem of modeling entity relationships by proposing DEER, a knowledge graph that uses free-text descriptions to explain relationships, and demonstrates that it can extract and generate high-quality descriptions without human annotation.
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.