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Identifying research schools using enriched bibliographical metadata

Published: 16 September 2014 Publication History

Abstract

Most scientific publications are easily accessible nowadays, but their accessibility alone does not yet provide researchers with all services they really need. Researchers investigating a new topic still spend a lot of time searching for the most relevant publications, persons or groups inside the respective community. Even experienced members of a community may know who to ask for explaining new techniques or what conference's proceedings to read, but still the relevant information they are interested in is not accessible at a glance. We claim that comprehensive bibliographical analysis services will help both new and experienced researchers to gain an overview of relevant publications, persons or groups within their community more easily. In this paper we present an approach to identifying research schools and their influence on each other, by quantitatively analysing citation graphs and Metadata of publications. Additionally, an outlook is provided towards automating this kind of analysis using linked data technology.

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Published In

cover image ACM Other conferences
i-KNOW '14: Proceedings of the 14th International Conference on Knowledge Technologies and Data-driven Business
September 2014
262 pages
ISBN:9781450327695
DOI:10.1145/2637748
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 September 2014

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Author Tags

  1. bibliographical analysis
  2. citation graph
  3. digital library
  4. linked data

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  • Research-article

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i-KNOW '14

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i-KNOW '14 Paper Acceptance Rate 25 of 73 submissions, 34%;
Overall Acceptance Rate 77 of 238 submissions, 32%

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