Call for papers: Special Issue on Question Answering over Linked Data
Call for papers: Special Issue on
Question Answering over Linked Data
The importance of the Semantic Web as a knowledge source is rapidly increasing. It is also gaining significant industrial relevance amongst content providers and search engine vendors. However, although the amount of linked data is growing, it is still used mostly by Semantic Web experts. One of the main reasons is that accessing the billions of RDF triples already available requires proficiency in the query language SPARQL as well as familiarity with the datasets available and their underlying schemas. These obstacles leave much of the available semantic knowledge inaccessible for most humans, which in turn diminishes the opportunities for its exploitation.
As a result, there is a growing amount of research on interaction paradigms that allow end users to access linked data and hide the complexity of Semantic Web standards behind an easy-to-use interface. Especially question answering systems play a major role, as they allow users to express arbitrarily complex information needs in an intuitive fashion and, at least in principle, in their native language.
The key challenge in providing end users with multilingual access to structured, linked data lies in interpreting the user's information need expressed in natural language with respect to the data that is queried. This involves challenges different from question answering over free text. In particular it requires dealing with the gap between natural language and data models, with distributed, heterogeneous datasets, and the use of background information and reasoning.
We solicit original papers addressing the challenges involved in question answering over linked data, presenting resources and tools to support question answering over linked data, or describing question answering systems and applications.
Particular topics of interest include (but are not restricted to):
- Natural language analysis
- Term matching and entity disambiguation
- Language resources for question answering (data as well as open-source software)
- SPARQL query generation
- Discovery of semantic sources
- Dealing with data and schema heterogeneity
- Dealing with incompleteness and inconsistencies
- Answer aggregation
- Answer rendering
- Providing justifications of answers and conveying trust
- User feedback and interaction
- Scalability and indexing of large datasets
- Knowledge base design for question answering
- Reasoning for question answering
- Keyword-based approaches
- Deadline: December 28, 2014 (extended!)
- Submissions shall be made through the Semantic Web journal website at http://www.semantic-web-journal.net. Prospective authors must take notice of the submission guidelines posted at http://www.semantic-web-journal.net/authors. Note that you need to request an account on the website for submitting a paper. Please indicate in the cover letter that it is for the "Question Answering over Linked Data" special issue.
- Submissions are possible in all standing paper type of the journal, see http://www.semantic-web-journal.net/authors for descriptions: full research papers, system descriptions, tool and dataset descriptions, surveys, application reports.
The guest editors can be contacted at email@example.com
- Christina Unger (CITEC, Bielefeld University)
- Axel-Cyrille Ngonga Ngomo (AKSW, University of Leipzig)
- Philipp Cimiano (CITEC, Bielefeld University)
- Sören Auer (University of Bonn, Fraunhofer IAIS)
- George Paliouras (NCSR Demokritos, Greece)
Guest Editorial board (to be confirmed and completed)
- Brigitte Grau (LIMSI/CNRS, France)
- Alan Aronson (National Library of Medicine, USA)
- Robert Hoehndorf (University of Aberystwyth, Wales)
- Dina Demner-Fushman (National Library of Medicine, USA)
- Andre Freitas (DERI National University of Ireland, Galway, Ireland)
- Gregory Grefenstette (INRIA, France)
- Allan Hanbury (Vienna University of Technology, Austria)
- Corina Forascu (University of Iasi, Romania)
- Elena Cabrio (INRIA, France)
- Edward Curry (INSIGHT, Ireland)
- Vanessa Lopez (IBM, Ireland)