Report on the First Knowledge Graph Reasoning Challenge 2018
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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 approach that formalized the problem as a constraint satisfaction problem and solved it using a lightweight formal method; the second 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.
KeywordsKnowledge graph Open data Reasoning Machine learning
We would like to express our gratitude to all the participants in the workshops, technical meetings, and other events that have been held so far. This work was supported by JSPS KAKENHI Grant Number 19H04168.
- 2.Brina, O., Cotton, C.: Explanation and justification in machine learning: a survey. In: Proceedings of IJCAI 2017 Workshop on Explainable AI (2017)Google Scholar
- 3.Fauna\(\_\)of\(\_\)India Wiki. https://en.wikipedia.org/wiki/Fauna_of_India. Accessed 18 Jan 2019
- 4.Mineshima, K., Tanaka, R., Gomez, P.M., Miyao, Y., Bekki, D.: Building compositional semantics and higher-order inference system for a wide-coverage Japanese CCG parser. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2236–2242 (2016)Google Scholar
- 5.Kitagawa, K., Shiramatsu, S., Kamiya, A.: Developing a method for quantifying degree of discussion progress towards automatic facilitation of web-based discussion. In: Lujak, M. (ed.) AT 2018. LNCS (LNAI), vol. 11327, pp. 162–169. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-17294-7_12CrossRefGoogle Scholar
- 6.Ikeda, Y., Shiramatsu, S.: Generating questions asked by facilitator agents using preceding context in web-based discussion. In: Proceedings of the 2nd IEEE International Conference on Agents, pp. 127–132 (2017)Google Scholar
- 7.Noble, D., Rittel, H.W.: Issue-based information systems for design. In: Proceedings of the Computing in Design Education, pp. 275–286 (1988)Google Scholar