Knowledge Graph Inference Method Combined with Decision Implication

Abstract

Decision implication is a tool of decision knowledge representation and reasoning in formal concept analysis. This paper proposes a relationship completion method for knowledge graph based on decision implication. Firstly, this paper constructs the corresponding decision context for a knowledge graph and proves that decision implications are able to equivalently represent the rules in knowledge graph inference. In order to efficiently extract decision implications, this paper reduces the complicated decision contexts many times and proves that the reduced decision contexts also contain the rules in knowledge graph inference. This paper also designs an algorithm to extract decision  implications from the reduced decision contexts and provides steps to perform relationship completion by applying decision implications. Finally, experiments verify the effectiveness of the proposed method. This paper provides a new idea for completing knowledge graph relationship, as well as a new choice for fusion inference.

Keywords

• formal concept analysis; decision implication; object reduction; knowledge graph; relationship completion