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Apologies for cross-posting.
==== Call for Papers ====
EKAW2018 Doctoral Consortium
Date: 13 November 2018
Venue: Nancy, France
Twitter Hashtag: #ekaw2018DC
Website:
https://project.inria.fr/ekaw2018/call-for-doctoral-consortium/
# IMPORTANT DATES
- Abstract deadline : 7 September, 23:59 Hawaii Time (sharp)
- Paper deadline : 14 September, 23:59 Hawaii Time (sharp)
- Notification : 5 October, 23:59 Hawaii Time (sharp)
The EKAW 2018 Doctoral Consortium is an opportunity for PhD
students in Knowledge Engineering and Knowledge Management to
discuss and obtain feedback on their ongoing work, plans and
research directions with/from experienced researchers in the
field. The objective is to share best practices of research
methods and approaches, as well as to exchange on what it means to
engage in an academic career on the topics relevant to the EKAW
conference.
All papers submitted to the EKAW 2018 Doctoral Consortium will be
reviewed by three experienced researchers. In addition, the
authors of accepted papers will be asked to review one of the
other Doctoral Consortium papers. The objective is to make
students experience the reviewing process and to provide for each
paper different views regarding the research they describe.
Submissions will be divided into two different categories
depending on the PhD phase:
- Early Stage PhD: Students who may have identified the main
research problem they want to address as well as the relevant
literature, and who are building their research methodology, but
who might not yet have obtained significant results, or only
preliminary ones.
- Late Stage PhD: Students who have already defined their approach
(even if incompletely) and obtained significant results (e.g.,
that might have been published already).
These categories do not affect the chances of being selected. They
will, however, be taken into account by the reviewers in their
feedback, and in the length and format of the presentation. The
organisers might decide to move a submission from one category to
the other, if they think it is justified.
Submission guidelines
All submissions must be single-author submissions. Please
acknowledge your PhD advisor(s) and other contributors in the
Acknowledgements section. Submissions should clearly indicate the
category of the submission (Early Stage PhD or Late Stage PhD) and
should be structured around the following items which are the key
methodological components required for a sound research narrative:
1. Problem: describe the core problem that you work on, motivate
its relevance for the knowledge management, knowledge acquisition
and knowledge representation areas, and formulate the research
question(s) and/or hypotheses that you will answer;
2. State of the art: describe relevant related work and point out
areas that need to be improved or investigated;
3. Proposed Approach: present the approach taken and motivate how
this is novel with respect to existing work;
4. Methodology: sketch the methodology that is (or will be)
adopted, including the evaluation protocol, i.e. the way in which
the results will be validated and/or the hypotheses will be
tested.
5. Results: describe the current status of the work and any
results that have been reached so far;
6. Discussion: reflect on why you think your approach will work
(or not), difficulties you have run into, and recommendations for
future work.
Topics
The Doctoral Consortium focuses on the same topics of the main
conference. In particular, but not exclusively, we solicit papers
about methods, tools and methodologies relevant with regard to the
following topics:
AI and Knowledge
- AI-based knowledge engineering and management
- Natural Language Processing and knowledge discovery/acquisition
- Knowledge acquisition for AI
- Intelligent knowledge evolution, maintenance, and repair
- Managing compliance between knowledge and data
- Managing Multimedia knowledge
- Machine Learning and the knowledge lifecycle
- Combining learning knowledge from data and from humans
- Modeling learned and conceptual knowledge together
- Lessons learned from case studies
- Adoption of techniques that exploit knowledge and AI
- Evaluation of techniques that exploit knowledge and AI
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
- Methods for accelerating take-up of knowledge management
technologies
- Corporate memories for knowledge management
- Knowledge evolution, maintenance and preservation
- Web 2.0 technologies for knowledge management
- Incentives for human knowledge acquisition (e.g. games with a
purpose)
Knowledge Engineering and Acquisition
- Tools and methodologies for ontology engineering
- Ontology design patterns
- Ontology localisation
- Ontology alignment
- Knowledge authoring and semantic annotation
- Knowledge acquisition from non-ontological resources (thesauri,
folksonomies, etc.)
- Semi-automatic knowledge acquisition, e.g., ontology learning
- 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
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
- Trust and privacy in knowledge representation
- Collaborative and social approaches to knowledge management and
acquisition
- Crowdsourcing in knowledge management
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)
Submission information and requirements
All submissions for the Doctoral Consortium must be in English,
and between 5 and 8 pages. Papers and abstracts can be submitted
electronically via EasyChair
(
http://www.easychair.org/conferences/?conf=ekaw2018doctoralcons).
Submissions must be either in PDF or in HTML, formatted in the
style of the Springer Publications format for Lecture Notes in
Computer Science (LNCS). For details on the LNCS style, see
Springer's Author Instructions at
http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0.
HTML submissions should be submitted to EasyChair as a ZIP archive
that contains the complete content of the paper. Authors can use
any HTML-based format for the submission, but a mandatory
LNCS-like layout should be provided and the submission still needs
to comply with the established page limit. Authors who are new to
HTML submissions may consider to use either dokieli
(
https://dokie.li) or RASH (
https://github.com/essepuntato/rash)
that can help produce well formatted academic papers using HTML
and are capable of rendering papers in the LNCS layout.
Students accepted to present at the Doctoral Consortium must
attend the Doctoral Consortium for the whole day in order to gain
as much value as possible from the experience. Each submitter
should also be aware that they will be asked to review one other
paper submitted to the Doctoral Consortium.
Accepted papers will be published online via CEUR Workshop
Proceedings (or equivalent).
Important Dates
Abstract submission: 7 September 2018
Full paper submission: 14 September 2018
Notification: 5 October 2018
Camera-ready: TBA
Doctoral Consortium: 13 November 2018
Chairs
Francesco Osborne (KMi, The Open University, UK)
Laura Hollink (CWI, Netherlands)
-- The Open University is incorporated by Royal Charter (RC
000391), an exempt charity in England & Wales and a charity
registered in Scotland (SC 038302). The Open University is
authorised and regulated by the Financial Conduct Authority in
relation to its secondary activity of credit broking.
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