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A Novel Logical Query Representation Learning Model on Knowledge Graph for Interpretable Knowledge Reasoning

Towards interpretable knowledge reasoning task, this paper aims at understanding the logical form of the given query, which is formulated as the first-order logical rule. Then this paper proposes a novel representation learning model for Interpretable Knowledge Reasoning (IKR), which consists of two procedures: Firstly, all the elements (including entities, relations and query) are embedded into the unified semantic spaces; Secondly, semantic similarity is utilized for measure the relevance between the given query and candidate entities, and then the answer entity set is generated. Experimental results on multi-type tasks demonstrate that our work is experimentally applicable for the first-logic rules with more complex length.