Report on the First Knowledge Graph Reasoning Challenge 2018 -- Toward the eXplainable AI System
Takahiro Kawamura, Shusaku Egami, Koutarou Tamura, Yasunori Hokazono,, Takanori Ugai, Yusuke Koyanagi, Fumihito Nishino, Seiji Okajima, Katsuhiko, Murakami, Kunihiko Takamatsu, Aoi Sugiura, Shun Shiramatsu, Shawn Zhang, and, Kouji Kozaki

TL;DR
This paper summarizes the 2018 Knowledge Graph Reasoning Challenge focused on explainable AI, highlighting various reasoning techniques, evaluation metrics, and results in the context of Sherlock Holmes story analysis.
Contribution
It presents the first challenge on knowledge graph reasoning for explainability, including methodology, evaluation, and diverse approaches proposed by participants.
Findings
Constraint satisfaction approach won first prize
SPARQL and rules approach was highly effective
Word embeddings and multi-agent models were innovative solutions
Abstract
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 becoming 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 characters 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, the techniques proposed for reasoning and/or estimation, the evaluation metrics, and the results. The first prize went to an…
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Taxonomy
TopicsSemantic Web and Ontologies · Topic Modeling · Biomedical Text Mining and Ontologies
