KGR4XAI

The 1st International Workshop on Knowledge Graph Reasoning for Explainable Artificial Intelligence
co-located with 10th International Joint Conference on Knowledge Graphs (IJCKG 2021)

Schedule

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, the Knowledge Graph Reasoning Challenge (KGRC) has been organized from 2018(*). It aims to promote techniques for explainable AI using knowledge graphs. The challenge provides a common task to estimate criminals with a reasonable explanation based on an open knowledge graph of a well-known Sherlock Holmes mystery story. Variety of systems and ideas were submitted such as the approach of constraint satisfaction problem solving, the approach of logical rules and machine learning techniques including knowledge graph embeddings. And we had a lot of fruitful discussions.

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.

Topic of interests

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

  • Reasoning on Knowledge Graph
  • Reasoning for Knowledge Graph construction and refinement, such as modeling, authoring, alignment, and completion
  • Machine Learning on Knowledge Graph
  • Machine Learning for Knowledge Graph construction and refinement
  • Explainable AI techniques using Knowledge Graph
  • 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
  • The other topics combined aboves

Submissions

Submissions must be in PDF format, using the latest ACM Proceedings Format with the default 9pt font (see sample-sigconf.tex or Interim layout.docx from ACM Primary Article Template). Paper submissions must be 4-8 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

  • Paper submission: 23:59 (Hawaii Time), November 17, 2021
  • Acceptance Notifications: November 23, 2021
  • Camera Ready Submissions: 23:59 (Hawaii Time), November 30, 2021
  • Conference Date: December 6, 2021

Workshop Organizing Committee

Kouji Kozaki, Osaka Electro-Communication University, Japan

Takahiro Kawamura, National Agriculture and Food Research Organization, Japan

Boris Villazón-Terrazas, Tinámica & International University of La Rioja (UNIR), 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

Boris Villazón-Terrazas, Tinámica & International University of La Rioja (UNIR), Spain

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, Japan

Kyoumoto Matsushita, Fujitsu, Japan

Takanori Ugai, Fujitsu, Japan

Chutiporn Anutariya, Asian Institute of Technology(AIT), Thailand

Janneth Chicaiza Espinosa, Universidad Técnica Particular de Loja, Ecuador

Keynote Speech

Natural Interactions and Knowledge in Virtual Coaching
Kristiina Jokinen

Senior Researcher
AI Research Center, AIST Tokyo Waterfront

Acknowledgement

This workshop is supported by a project, JPNP20006, commissioned by the New Energy and Industrial Technology Development Organization (NEDO).

Contact

kgr4xai@knowledge-graph.jp