KSEM 2020: Difference between revisions
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{{Event | {{Event | ||
|Acronym=KSEM 2020 | |Acronym=KSEM 2020 | ||
|Title= | |Title=International Conference on Knowledge Science, Engineering and Management | ||
|Series=KSEM | |Series=KSEM | ||
|Type=Conference | |Type=Conference | ||
|Start date=2020/08/28 | |Start date=2020/08/28 | ||
|End date=2020/08/30 | |End date=2020/08/30 | ||
| Line 10: | Line 9: | ||
|Homepage=http://ksem2020.org/ | |Homepage=http://ksem2020.org/ | ||
|City=Hangzhou | |City=Hangzhou | ||
|Country= | |Country=Online | ||
|Paper deadline=2020/04/16 | |Paper deadline=2020/04/16 | ||
|Notification=2020/05/25 | |Notification=2020/05/25 | ||
| Line 18: | Line 17: | ||
|has program chair=Gang Li, Hengtao Shen, Ye Yuan | |has program chair=Gang Li, Hengtao Shen, Ye Yuan | ||
}} | }} | ||
== Topics of Interest == | |||
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 | |||
• Logics of knowledge; formal analysis of knowledge; reasoning about knowledge | |||
• Knowledge complexity and knowledge metrics | |||
• Common sense knowledge; non-monotonic reasoning | |||
• Uncertainty in knowledge (randomness, fuzziness, roughness, vagueness) | |||
• Machine learning | |||
• Formal ontologies | |||
-- | • Integration of machine learning and knowledge representation | ||
• Reasoning about knowledge in the presence of inconsistency, incompleteness and context-dependency | |||
• Belief revision and updates | |||
• Cognitive foundations of knowledge | |||
• Knowledge in complex systems (e.g. economical and quantum systems) | |||
• Game-theoretical aspects of knowledge; knowledge in multi-agent systems | |||
=== Knowledge Engineering === | |||
• Knowledge extraction from texts/big data/Web | |||
• Knowledge discovery from very large databases | |||
• Knowledge integration | |||
• Knowledge-based software engineering | |||
• Knowledge-based systems in life sciences | |||
• Conceptual modelling in knowledge-based systems | |||
• Semantic database systems | |||
• Semantic Web: Content and ontological engineering | |||
• Knowledge engineering applications | |||
=== Knowledge Management === | |||
• Knowledge creation and acquisition | |||
• Knowledge verification and validation | |||
• Knowledge dissemination | |||
• Knowledge management systems | |||
• Knowledge and data integration | |||
• Knowledge adaptation | |||
• Knowledge management best practices and applications | |||
=== Knowledge Graphs === | |||
• Probabilistic Knowledge Graphs | |||
• Knowledge graph construction | |||
• Knowledge graph query | |||
• Knowledge graph storage, query and management | |||
• Learning on knowledge graphs | |||
• Knowledge graph embedding | |||
• Knowledge graph completion | |||
• Knowledge graph link prediction | |||
• Knowledge graph applications | |||
Latest revision as of 13:47, 11 June 2020
| KSEM 2020 | |
|---|---|
International Conference on Knowledge Science, Engineering and Management
| |
| Event in series | KSEM |
| Dates | 2020/08/28 (iCal) - 2020/08/30 |
| Homepage: | http://ksem2020.org/ |
| Location | |
| Location: | Hangzhou, Online |
| Important dates | |
| Papers: | 2020/04/16 |
| Submissions: | 2020/04/16 |
| Notification: | 2020/05/25 |
| Camera ready due: | 2020/06/10 |
| Committees | |
| General chairs: | Hai Jin, Xuemin Lin, Xun Wang |
| PC chairs: | Gang Li, Hengtao Shen, Ye Yuan |
| Table of Contents | |
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Topics of Interest
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
• Logics of knowledge; formal analysis of knowledge; reasoning about knowledge
• Knowledge complexity and knowledge metrics
• Common sense knowledge; non-monotonic reasoning
• Uncertainty in knowledge (randomness, fuzziness, roughness, vagueness)
• Machine learning
• Formal ontologies
• Integration of machine learning and knowledge representation
• Reasoning about knowledge in the presence of inconsistency, incompleteness and context-dependency
• Belief revision and updates
• Cognitive foundations of knowledge
• Knowledge in complex systems (e.g. economical and quantum systems)
• Game-theoretical aspects of knowledge; knowledge in multi-agent systems
Knowledge Engineering
• Knowledge extraction from texts/big data/Web
• Knowledge discovery from very large databases
• Knowledge integration
• Knowledge-based software engineering
• Knowledge-based systems in life sciences
• Conceptual modelling in knowledge-based systems
• Semantic database systems
• Semantic Web: Content and ontological engineering
• Knowledge engineering applications
Knowledge Management
• Knowledge creation and acquisition
• Knowledge verification and validation
• Knowledge dissemination
• Knowledge management systems
• Knowledge and data integration
• Knowledge adaptation
• Knowledge management best practices and applications
Knowledge Graphs
• Probabilistic Knowledge Graphs
• Knowledge graph construction
• Knowledge graph query
• Knowledge graph storage, query and management
• Learning on knowledge graphs
• Knowledge graph embedding
• Knowledge graph completion
• Knowledge graph link prediction
• Knowledge graph applications