Ranking & Metrics Conference Call for Papers Other Conferences in United States
K-CAP 2021 : Knowledge Capture Conference

K-CAP 2021 : Knowledge Capture Conference

Online, Online

Submission Deadline: Wednesday 15 Sep 2021

Conference Dates: Dec 02, 2021 - Dec 03, 2021

Research
Impact Score 2.48

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Ranking & Metrics Impact Score is a novel metric devised to rank conferences based on the number of contributing top scientists in addition to the h-index estimated from the scientific papers published by top scientists. See more details on our methodology page.

Research Impact Score: 2.48
Contributing Top Scientist: 11
Papers published by Top Scientists 29
Research Ranking (Computer Science) 363

Conference Call for Papers

Topics of interest
Knowledge acquisition
Knowledge authoring
Knowledge extraction
Knowledge management
Knowledge publication
Knowledge capture from multimodal data, e.g., text, audio, visual
Knowledge capture for the Semantic Web and the Web of Linked Data
Knowledge capture and enrichment in specific domains such as earth and life sciences
Collaborative and social approaches to knowledge management and acquisition
Crowdsourcing for knowledge capture and refinement
Handling bias in knowledge capture
Knowledge capture from social environments and contexts
Problem-solving knowledge and methods
Knowledge-based markup techniques
Modeling methodologies for ontologies and knowledge graphs
Narrative intelligence
Knowledge capture through storytelling
Provenance and trust issues in knowledge intensive systems
Services and applications that enable or utilize data capture techniques
Semantic search and query enrichment
Knowledge engineering and knowledge governance
Ontology design patterns
Similarity measurement and analogy-based reasoning
Extracting knowledge graphs from unstructured/semi-structured and multimedia data
Summarization techniques for knowledge graphs
Visualization of knowledge graphs and visual query interfaces
Services and API for knowledge graphs
Addressing scalability issues for distributed knowledge graphs
Automated construction, enrichment and cleaning of knowledge graphs and alignment to existing graphs
Hybrid approaches for knowledge capture combining knowledge engineering and machine learning
Data dynamicity, heterogeneity, and decay in knowledge intensive systems

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