Difference between revisions of "EKAW 2020"

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*Ontologies for trust and ethics
 
*Ontologies for trust and ethics
 
*Trust and privacy in knowledge representation<br>
 
*Trust and privacy in knowledge representation<br>
 
  
  
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*Uncertainty and vagueness in knowledge representation
 
*Uncertainty and vagueness in knowledge representation
 
*Dealing with dynamic, distributed and emerging knowledge <br>
 
*Dealing with dynamic, distributed and emerging knowledge <br>
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'''Knowledge Management'''
 
'''Knowledge Management'''
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*Knowledge evolution, maintenance and preservation
 
*Knowledge evolution, maintenance and preservation
 
*Incentives for human knowledge acquisition and data quality improvement (e.g. games with a purpose) <br>
 
*Incentives for human knowledge acquisition and data quality improvement (e.g. games with a purpose) <br>
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'''Social and Cognitive Aspects of Knowledge Representation'''
 
'''Social and Cognitive Aspects of Knowledge Representation'''
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*Collaborative and social approaches to knowledge management and acquisition
 
*Collaborative and social approaches to knowledge management and acquisition
 
*Crowdsourcing in knowledge management <br>
 
*Crowdsourcing in knowledge management <br>
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'''Knowledge discovery'''
 
'''Knowledge discovery'''
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*Classification and clustering for knowledge management
 
*Classification and clustering for knowledge management
 
*Symbolic and sub-symbolic learning machine learning <br>
 
*Symbolic and sub-symbolic learning machine learning <br>
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'''Applications in specific domains such as'''
 
'''Applications in specific domains such as'''

Revision as of 12:03, 17 February 2020

EKAW 2020
22nd International Conference on Knowledge Engineering and Knowledge Management
Event in series EKAW
Dates 2020/09/16 (iCal) - 2020/09/20
Homepage: https://ekaw2020.inf.unibz.it/
Location
Location: Bozen-Bolzano, Italy
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Table of Contents


22nd International Conference on Knowledge Engineering and Knowledge Management

Topics

Ethical and Trustworthy Knowledge Engineering

  • Ethics and trust in automated reasoning
  • Algorithmic transparency and explanations for knowledge-based systems
  • Knowledge and ethics
  • Ontologies for trust and ethics
  • Trust and privacy in knowledge representation


Knowledge Engineering and Acquisition

  • Tools and methodologies for ontology engineering
  • Ontology design patterns
  • Ontology localisation
  • Multilinguality in ontologies
  • Ontology alignment
  • Knowledge authoring and semantic annotation
  • Knowledge acquisition from non-ontological resources (thesauri, folksonomies, etc.)
  • Semi-automatic knowledge acquisition, e.g., ontology learning
  • Collaborative knowledge acquisition and formalisation
  • Mining the Semantic Web and the Web of Data
  • Ontology evaluation and metrics
  • Uncertainty and vagueness in knowledge representation
  • Dealing with dynamic, distributed and emerging knowledge


Knowledge Management

  • Methodologies and tools for knowledge management
  • Knowledge sharing and distribution, collaboration
  • Best practices and lessons learned from case studies
  • Provenance and trust in knowledge management
  • FAIR data and knowledge
  • Methods for accelerating take-up of knowledge management technologies
  • Corporate memories for knowledge management
  • Knowledge evolution, maintenance and preservation
  • Incentives for human knowledge acquisition and data quality improvement (e.g. games with a purpose)


Social and Cognitive Aspects of Knowledge Representation

  • Similarity and analogy-based reasoning
  • Knowledge representation inspired by cognitive science
  • Synergies between humans and machines
  • Knowledge emerging from user interaction and networks
  • Knowledge ecosystems
  • Expert finding, e.g., by social network analysis
  • Collaborative and social approaches to knowledge management and acquisition
  • Crowdsourcing in knowledge management


Knowledge discovery

  • Mining patterns and association rules
  • Mining complex data: numbers, sequences, trees, graphs
  • Formal Concept Analysis and extensions
  • Numerical data mining methods and knowledge processing
  • Mining the web of data for knowledge construction
  • Text mining and ontology engineering
  • Classification and clustering for knowledge management
  • Symbolic and sub-symbolic learning machine learningÂ


Applications in specific domains such as

  • eGovernment and public administration
  • Life sciences, health and medicine
  • Humanities and Social Sciences
  • Automotive and manufacturing industry
  • Cultural heritage
  • Digital libraries
  • Geosciences
  • ICT4D (Knowledge in the developing world)


Submissions

Important Dates

Committees

  • Co-Organizers
  • General Co-Chairs
  • Local Organizing Co-Chairs
  • Program Committee Members