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KGR4XAI 2023 : The 2nd International Workshop on Knowledge Graph Reasoning for Explainable Artificial Intelligence

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Link: https://kgr4xai.ikgrc.org/2023/
 
When Dec 9, 2023 - Dec 9, 2023
Where Miraikan, Tokyo, Japan
Submission Deadline Oct 30, 2023
Notification Due Nov 13, 2023
Final Version Due Nov 20, 2023
Categories    knowledge graphs   semantic web   ontology   artificial intelligence
 

Call For Papers

== DEADLINE EXTENSION ==

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 since 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

[Workshop type]
Hybrid

[Topic of interest]
Potential topics of interest 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 above

[Submission Guidelines]
All papers must be original and not simultaneously submitted to another journal or conference.
Submissions must be formatted in the style of CEURART ( https://ceurws.wordpress.com/2020/03/31/ceurws-publishes-ceurart-paper-style/ ) 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 ( https://ijckg2023.knowledge-graph.jp/ ) conference and present the paper in the workshop.
Papers can be submitted electronically via EasyChair ( https://easychair.org/my/conference?conf=kgr4xai2023 ).
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.

[Program Committee]
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, Japan
Kyoumoto Matsushita, Fujitsu, Japan
Takanori Ugai, Fujitsu, Japan
Janneth Chicaiza, Universidad Técnica Particular de Loja, Ecuador

[Organizing Committee]
Shusaku Egami, National Institute of Advanced Industrial Science and Technology, 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

[Contact]
Shusaku Egami, s-egami@aist.go.jp

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