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Extracting scientific trends by mining topics from Call for Papers
- Source :
- Library Hi Tech. 40:115-132
- Publication Year :
- 2019
- Publisher :
- Emerald, 2019.
-
Abstract
- PurposeThe purpose of this paper is to present a novel approach for mining scientific trends using topics from Call for Papers (CFP). The work contributes a valuable input for researchers, academics, funding institutes and research administration departments by sharing the trends to set directions of research path.Design/methodology/approachThe authors procure an innovative CFP data set to analyse scientific evolution and prestige of conferences that set scientific trends using scientific publications indexed in DBLP. Using the Field of Research code 804 from Australian Research Council, the authors identify 146 conferences (from 2006 to 2015) into different thematic areas by matching the terms extracted from publication titles with the Association for Computing Machinery Computing Classification System. Furthermore, the authors enrich the vocabulary of terms from the WordNet dictionary and Growbag data set. To measure the significance of terms, the authors adopt the following weighting schemas: probabilistic, gram, relative, accumulative and hierarchal.FindingsThe results indicate the rise of “big data analytics” from CFP topics in the last few years. Whereas the topics related to “privacy and security” show an exponential increase, the topics related to “semantic web” show a downfall in recent years. While analysing publication output in DBLP that matches CFP indexed in ERA Core A* to C rank conference, the authors identified that A* and A tier conferences not merely set publication trends, since B or C tier conferences target similar CFP.Originality/valueOverall, the analyses presented in this research are prolific for the scientific community and research administrators to study research trends and better data management of digital libraries pertaining to the scientific literature.
- Subjects :
- Topic model
Vocabulary
business.industry
Computer science
media_common.quotation_subject
Data management
05 social sciences
Big data
WordNet
02 engineering and technology
Scientific literature
Library and Information Sciences
Digital library
Data science
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
0509 other social sciences
050904 information & library sciences
business
Semantic Web
Information Systems
media_common
Subjects
Details
- ISSN :
- 07378831
- Volume :
- 40
- Database :
- OpenAIRE
- Journal :
- Library Hi Tech
- Accession number :
- edsair.doi.dedup.....b786977abc8a016c1511efedf5af4b5d