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:

Knowledge Science

• Knowledge representation and reasoning
• Formal analysis of knowledge; reasoning about knowledge
• Knowledge complexity and knowledge metrics
• 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-based system for Chatbot
• Knowledge-based system and Large Language Models (LLMs)

Knowledge Management

• Knowledge management best practices and applications
• Knowledge verification and validation (e.g. Blockchain)
• Knowledge protection and attack detection
• Knowledge-Based Systems in Cybersecurity Threat Detection and Intelligent Decision-Making
• Smart knowledge and resource optimization
• Knowledge dissemination
• Knowledge management systems
• Knowledge and data integration
• Knowledge adaptation

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