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Call for Papers! Special Issue on Models of Discovery: Mining the scientific literature

 

The Journal of Data and Information Science (JDIS) is honored to invite Professors Neil R. Smalheiser and Aaron M. Cohena to be Guest Editors for the Special Issue on Models of Discovery: Mining the scientific literature.

We call for papers that employ innovative conceptual or methodological approaches to discovering new knowledge, using the scientific literature as the source. Possible topics include:

l  Characterizing the core vs. the periphery of a discipline;

l  Identifying research areas that are particularly promising or particularly neglected;

l  Machine learning, including graph-based, tensor-based and deep learning approaches, active learning, merging heterogeneous textual and biological data, etc.;

l  Applying advanced NLP such as sentiment analysis, argumentation analysis, or recognizing humor and sarcasm;

l  Use of outside information to guide discovery, such as bibliometrics, distant supervision, or cross-corpus information;

l  Use of implicit information to guide discovery;

l  Evidence synthesis and summarization techniques to guide discovery;

l  Identifying findings that have low credibility or are unlikely to be true.

 

All manuscripts submitted to the special issue will be reviewed in accordance with standard journal procedures and be finally evaluated by the Issue Editors and Co-EICs.

 

All manuscripts should be submitted via JDIS’s online manuscript processing system: https://mc03.manuscriptcentral.com/jdis, and state "Special Issue on Models of Discovery: Mining the scientific literature” in the cover letter.

 

Issue Editors:

Prof. Neil R. Smalheiser

University of Illinois at Chicago, USA

Email: neils@uic.edu

 

Prof. Aaron M. Cohen

Oregon Health & Science University, USA
E-mail:
cohenaa@ohsu.edu

 

Important dates:

Expected publication date: Sept. 1, 2017

Deadline for submission: Jul. 1, 2017

  

If you have any suggestions or questions about the special issue, please feel free to contact us at liufh@mail.las.ac.cn or jdis@mail.las.ac.cn

 

We are looking forward to receiving your submissions! 

     The JDIS Editorial Office

 
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MINING DEEP KNOWLEDGE FROM SCIENTIFIC NETWORKS
School of Information Resource Management, Renmin University of China
Department of Information Management, School of Government of Beijing Normal University

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