Difference between revisions of "EKAW 2020"
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==Topics== | ==Topics== | ||
| − | Ethical and Trustworthy Knowledge Engineering | + | '''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 <br> | |
| − | Knowledge Engineering and Acquisition | + | '''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 <br> | |
| − | Knowledge Management | + | '''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) <br> | |
| − | Social and Cognitive Aspects of Knowledge Representation | + | '''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 <br> | |
| − | Knowledge discovery | + | '''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 learning <br> | |
| − | Applications in specific domains such as | + | '''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)<br> | ||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
==Submissions== | ==Submissions== | ||
Revision as of 12:02, 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 |
| 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
- some person, some affiliation, country
- PC Co-Chairs
- some person, some affiliation, country
- Workshop Chair
- some person, some affiliation, country
- Panel Chair
- some person, some affiliation, country
- Seminars Chair
- some person, some affiliation, country
- Demonstration Co-Chairs
- some person, some affiliation, country
- some person, some affiliation, country
- Local Organizing Co-Chairs
- some person, some affiliation, country
- Program Committee Members
- some person, some affiliation, country