KGR4XAI

The 2nd International Workshop on Knowledge Graph Reasoning for Explainable Artificial Intelligence
co-located with 11th International Joint Conference on Knowledge Graphs (IJCKG 2023)
workshop type: Hybrid

Proceedings

Workshop Proceedings of the IJCKG 2023 has been published

Schedule

Program

Scope

Machine 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. On this background, for example, the Knowledge Graph Reasoning Challenge (KGRC) has been organized from 2018*, **. It aims to promote techniques for explainable AI using knowledge graphs.

In this workshop we would like to discuss a wider variety of knowledge graph reasoning technologies for explainable AI in various domains. Although one typical topic is to solve mystery stories in the KGRC, knowledge graphs and related technologies in other domains are also welcome.

*Kawamura T. et al. (2020) Report on the First Knowledge Graph Reasoning Challenge 2018. In: Wang X., Lisi F., Xiao G., Botoeva E. (eds) Semantic Technology. JIST 2019. Lecture Notes in Computer Science, vol 12032. Springer, Cham.

**International Knowledge Graph Reasoning Challenge (IKGRC2023) co-located with IEEE ICSC2023

Topic of interests

Potential topics of interests include, but are not limited to:

  • Reasoning on Knowledge Graphs
  • Reasoning for Knowledge Graph construction and refinement, such as modeling, authoring, alignment, and completion
  • Knowledge Graph construction for reasoning
  • Machine Learning on Knowledge Graphs
  • Machine Learning for Knowledge Graph construction and refinement
  • Explainable AI techniques using Knowledge Graphs
  • Explainable AI techniques for Knowledge Graph construction and refinement
  • Knowledge Graph construction and refinement using reasoning, Machine Learning and Explainable AI techniques
  • Knowledge Graph construction and refinement for reasoning, Machine Learning and Explainable AI techniques
  • Knowledge Graph application and platform using reasoning, Machine Learning and Explainable AI techniques
  • Domain-dependent Knowledge Graph using reasoning, Machine Learning and Explainable AI techniques
  • Semantic system and tool for reasoning, Machine Learning and Explainable AI techniques
  • Ontology design and modelling for reasoning, Machine Learning and Explainable AI techniques
  • Knowledge graph-enhanced large language models
  • Semantic technologies for Generative AI
  • The other topics combined aboves

Submissions

Submissions must be formatted in the style of CEURART 1-column style. The title should use the emphasizing capitalized style and the paper should not include page numbers.

Submissions must be 8-16 pages, including references. (Short papers: 8 pages, Long papers: 16 pages)

At least one author of each accepted paper must register for the IJCKG conference and present the paper in the workshop.

Papers can be submitted electronically via EasyChair.

Accepted papers will be published on the workshop website. After the conference, the papers will be proposed for publishing at CEUR Workshop Proceedings. Papers for which authors do not register and present may be excluded from the proceedings.

Important Dates

  • (IJCKG Acceptance Notifications: October 6 October 21, 2023)
  • Paper submission: 23:59 (AoE), October 23 October 30, 2023
  • Acceptance Notifications: November 6 November 13, 2023
  • Camera Ready Submissions: 23:59 (AoE), November 20, 2023
  • Workshop Date: December 9, 2023

Workshop Organizing Committee

Shusaku Egami, Natioanl Institute of Advanced Industrial Science and Technology (AIST), Japan

Kouji Kozaki, Osaka Electro-Communication University, Japan

Takahiro Kawamura, National Agriculture and Food Research Organization, Japan

Boris Villazón-Terrazas, EY, Spain

Marut Buranarach, National Electronics and Computer Technology Center, Thailand

Program Committee Members

Kouji Kozaki, Osaka Electro-Communication University, Japan

Takahiro Kawamura, National Agriculture and Food Research Organization, Japan

Marut Buranarach, National Electronics and Computer Technology Center, Thailand

Shusaku Egami, National Institute of Advanced Industrial Science and Technology, Japan

Ken Fukuda, National Institute of Advanced Industrial Science and Technology (AIST), Japan

Kyoumoto Matsushita, Fujitsu, Japan

Takanori Ugai, Fujitsu, Japan

Janneth Chicaiza, Universidad Técnica Particular de Loja, Ecuador

Acknowledgement

This workshop is supported by a project, JPNP20006, commissioned by the New Energy and Industrial Technology Development Organization (NEDO), and Japan Society Promotion of Science (JSPS) KAKENHI Grant Number JP19H04168.

Contact

kgr4xai@knowledge-graph.jp