[InetBib] Cfp: Special Issue on Bibliometric-Enhanced Information Retrieval and Natural Language Processing for Digital Libraries

["Mayr-Schlegel, Philipp" <Philipp.Mayr-Schlegel@xxxxxxxxx>]

Call for Papers: Special Issue on Bibliometric-Enhanced Information Retrieval 
and Natural Language Processing for Digital Libraries
to be published in the International Journal on Digital Libraries (IJDL) 
<http://www.springer.com/799>

Important Dates:
- September 30, 2016 Paper submission deadline
- November 15, 2016 First notification
- January 15, 2017 Revision submission
- March 15, 2017 Second notification
- April 1, 2017 Final version submission

Current digital libraries collect and allow access to digital papers and their 
metadata - but mostly do not analyze the full-text of the materials they index. 
The scale of scholarly publications poses a challenge for scholars in their 
search for relevant literature.

This special issue calls for new, unpublished article submissions on the 
analysis of scholarly publications and data, in the context of the explosion in 
the production of scientific literature and the growth of scientific 
enterprise. Articles in the issue will investigate how natural language 
processing, information retrieval, scientometric and recommendation techniques 
can advance the state of the art in scholarly document understanding, analysis 
and retrieval at scale. Researchers are in need of assistive technologies to 
track developments in an area, identify the approaches used to solve a research 
problem over time and summarize research trends. Digital libraries require 
semantic search, question answering and automated recommendation and reviewing 
systems to manage and retrieve answers from scholarly databases. Full document 
text analysis can help to design semantic search, translation and summarization 
systems; citation and social network analyses can help digital libraries to 
visualize scientific trends, bibliometrics and relationships and influences of 
works and authors. All these approaches can be supplemented with the metadata 
supplied by digital libraries - such as the article title, journal or 
conference name, author information, language, datasets, keywords, section 
headers, citation relationships, topic terms - and even browsing and usage 
data, such as related search queries and download counts.

The issue aims to bring together the three communities of digital libraries 
(DL), information retrieval (IR) and natural language processing (NLP) to 
discuss the potential of automated textual analysis and bibliometrics to 
enhance scholarly discovery process. We thus are soliciting high-quality, 
previously unpublished submissions on topics including - but not limited to - 
full-text, multimedia and/or multilingual analysis of scholarly publications, 
as well as citation-based NLP or IR. Example fields of interests include (but 
are not limited to):

- Summarization of scientific articles; automatic creation of reviews and 
automatic qualitative
assessment of submissions; question-answering for scholarly DLs
- Text and data mining technologies of scholarly articles to facilitate 
browsing and information-seeking
- Recommendation for scholarly papers, reviewers, citations and publication 
venues
- Navigation, searching and browsing in scholarly DLs; niche search in 
scholarly DLs; new
information access methods for scientific papers
- Network analysis and citation analysis in scholarly DLs; citation 
function/motivation analysis;
novel bibliometric metrics; topical modeling analysis; information retrieval 
for scholarly text,
e.g. citation-based IR
- Knowledge discovery and analysis of information provenance
- Translation, multilingual and multimedia analysis and alignment of scholarly 
works; analyses of
writing style in scholarly publications
- Methods for and applications of the automatic mining and discovery of 
structured and
unstructured metadata
- Domain vocabularies and taxonomies for resource description and discovery
- Disambiguation issues in scholarly DLs using NLP or IR techniques; data 
cleaning and data quality


Guest Editors
Guillaume Cabanac, University of Toulouse, France
Muthu Kumar Chandrasekaran, NUS School of Computing, Singapore
Ingo Frommholz, University of Bedfordshire, UK
Kokil Jaidka, Adobe Systems Inc., India
Min-Yen Kan, NUS School of Computing, Singapore
Philipp Mayr, GESIS - Leibniz Institute for the Social Sciences, Cologne, 
Germany
Dietmar Wolfram, University of Wisconsin-Milwaukee, USA

Paper Submission
Papers submitted to this special issue for possible publication must be 
original and must not be under
consideration for publication in any other journal or conference. Previously 
published or accepted
conference papers must contain at least 30% new material to be considered for 
the special issue.
All papers are to be submitted by referring to http://www.springer.com/799. At 
the beginning of the
submission process, under "Article Type", please select the appropriate special 
issue. All manuscripts
must be prepared according to the journal publication guidelines which can also 
be found on the
website provided above. Papers will be reviewed following the journal's 
standard review process.
Please address inquiries to Min-Yen Kan at knmnyn@xxxxxxxxx.

cfp on the Springer website:
<http://static.springer.com/sgw/documents/1558268/application/pdf/Bibliometric-enhanced+IR+and+NLP+for+DL.pdf>

--
Dr. Philipp Mayr
Team Leader

GESIS - Leibniz Institute for the Social Sciences
Unter Sachsenhausen 6-8,  D-50667 Köln, Germany
Tel: + 49 (0) 221 / 476 94 -533
Email: philipp.mayr@xxxxxxxxx
Web: http://www.gesis.org

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