CfP for EKAW2018 Doctoral Consortium

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: 19 October 2018
Doctoral Consortium: 13 November 2018

Chairs
Francesco Osborne (KMi, The Open University, UK)
Laura Hollink (CWI, Netherlands)

Received on Thursday, 30 August 2018 10:09:24 UTC