Theoretical Rule-based Knowledge Graph Reasoning by Connectivity Dependency Discovery
Canlin Zhang, Chun‐Nan Hsu, Yannis Katsis, Ho‐Cheol Kim, Yoshiki Vázquez‐Baeza · 2022 International Joint Conference on Neural Networks (IJCNN) · 2022
Discovering precise and interpretable rules from knowledge graphs is regarded as an essential challenge, which can improve the performances of many downstream tasks and even provide new ways to approach some Natural Language Processing research topics. In this paper, we present a fundamen-tal theory for rule-based knowledge graph reasoning, based on which the connectivity dependencies in the graph are captured via multiple rule types. It is the first time for some of these rule types in a knowledge graph to be considered. Based on these rule types, our theory can provide precise interpretations to unknown triples. Then, we implement our theory by what we call the RuleDict model. Results show that our RuleDict model not only provides precise rules to interpret new triples, but also achieves state-of-the-art performances on one benchmark knowledge graph completion task, and is competitive on other tasks.