DEER: Descriptive Knowledge Graph for Explaining Entity Relationships
Jie Huang, Kerui Zhu, Kevin Chen-Chuan Chang, Jinjun Xiong, Wen-mei, Hwu

TL;DR
DEER introduces a self-supervised approach to create an open, descriptive knowledge graph by generating free-text relation descriptions, eliminating the need for human annotation.
Contribution
The paper presents a novel self-supervised method for extracting and synthesizing relation descriptions to build an informative knowledge graph.
Findings
High-quality relation descriptions are effectively extracted and generated.
The system operates without human-labeled data.
DEER enables open and descriptive knowledge graph construction.
Abstract
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…
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Taxonomy
TopicsTopic Modeling · Explainable Artificial Intelligence (XAI) · Biomedical Text Mining and Ontologies
