Report on the First Knowledge Graph Reasoning Challenge 2018 -- Toward the eXplainable AI System

A new challenge for knowledge graph reasoning started in 2018. Deep learning has promoted the application of artificial intelligence (AI) techniques to a wide variety of social problems. Accordingly, being able to explain the reason for an AI decision is b ecoming important to ensure the secure and safe use of AI techniques. Thus, we, the Special Interest Group on Semantic Web and Ontology of the Japanese Society for AI, organized a challenge calling for techniques that reason and/or estimate which character s are criminals while providing a reasonable explanation based on an open knowledge graph of a well - known Sherlock Holmes mystery story . This paper presents a summary report of the first challenge held in 2018, including the knowledge graph construction, t he techniques proposed for reasoning and/or estimation, the evaluation metrics, and the results. The first prize went to an approach that formalized the problem as a constraint satisfaction problem and solved it using a lightweight formal method; the secon d prize went to an approach that used SPARQL and rules; the best resource prize went to a submission that constructed word embedding of characters from all sentences of Sherlock Holmes novels; and the best idea prize went to a discussion multi - agents model . We conclude this paper with the plans and issues for the next challenge in 2019.