KSEM 2023: The 16th International Conference on Knowledge Science, Engineering and Management

August 16-18, 2023, Guangzhou, China

https://www.ksem2023.conferences.academy/

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Important Dates

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Paper submission: April 28, 2023

Author notification: May 25, 2023

Camera-Ready: June 11, 2023

 

CALL FOR PAPERS

The aim of this interdisciplinary conference is to provide a forum for researchers in the broad areas of knowledge science, knowledge engineering, and knowledge management to exchange ideas and to report state of the art research results. KSEM is in the list of CCF (China Computer Federation) recommended Conferences (C series, Artificial Intelligence). The conference committee invites submissions of applied or theoretical research as well as of application-oriented papers on all the topics of KSEM. Topics include, but are not limited to the following:

第16届知识科学、工程与管理国际会议（KSEM 2023）将于8月16-18日在广州召开。KSEM系列会议由陆汝钤院士创办，被中国计算机学会推荐为C类会议，目前已举办十五届。KSEM系列会议的主要目的是为人工智能相关领域的研究人员提供一个论坛，用来交流人工智能领域相关人员的最新想法和报告最新研究成果。会议征稿的主题包括但不限于以下内容：

 

Knowledge Science

• Knowledge representation and reasoning
• Formal analysis of knowledge and reasoning about knowledge
• Knowledge complexity and knowledge onotonic reasoning
• Uncertainty in knowledge (randomness, fuzziness, roughness, vagueness)
• Knowledge fusion for decision making
• Formal ontologies
• Reasoning about knowledge in the presence of inconsistency, incompleteness and context-dependency
• Belief propagation, revision and aggregation
• Cognitive foundations of knowledge
• Integration of machine learning and knowledge representation
• Knowledge-driven learning
• Knowledge for cognitive robotics
• Knowledge for cognitive analytics
• Knowledge in complex systems (e.g. manufacture assembling, economical and quantum systems)
• Game-theoretical aspects of knowledge; knowledge in multi-agent systems

 

Knowledge Engineering

• Knowledge modeling
• Knowledge acquisition, such as knowledge modules, temporal knowledge, etc.
• Knowledge extraction from texts/videos, big data/Web
• Knowledge discovery from very large databases
• Knowledge integration
• Knowledge-based software engineering
• Knowledge-based systems in life sciences
• Knowledge-based systems for smart homes
• Conceptual modeling in knowledge-based systems
• Semantic database systems
• Semantic Web (Content and ontological engineering)
• Knowledge engineering applications
• Knowledge modeling for digital twins

 

Knowledge Management

• Knowledge management best practices and applications
• Knowledge verification and validation (e.g. Blockchain)
• Knowledge protection and anomaly detection
• Smart knowledge and resource optimization
• Knowledge dissemination
• Knowledge management systems
• Knowledge and data integration
• Knowledge adaptation
• Knowledge creation and acquisition

 

Knowledge Graphs

• Knowledge graph storage and management
• Probabilistic knowledge graphs
• Knowledge graph construction
• Knowledge graph query
• Learning on knowledge graphs
• Knowledge graph embedding
• Knowledge graph completion
• Multi-modal knowledge graphs
• Knowledge graph applications
• Deep graph neural networks

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作者：蒋运承
来源：学者网
原文链接：https://www.scholat.com/vpost.html?pid=206891