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Title:Deriving dynamic knowledge from academic social tagging data: A novel research direction
Author(s):Dong, Hang; Wang, Wei; Coenen Frans
Subject(s):Academic social tagging
Data cleaning
Ontology learning
Concept extraction
Knowledge evolution
Abstract:Academic social tagging is an important activity in the age of Web 2.0 (and Science 2.0) whereby researchers collaborate online to organize academic resources. Compared to general social tagging, academic social tagging has a more complex nature in terms of semantics and sparsity of the data. It is worth exploring the knowledge structure hidden in the academic social tags and to see how they reflect the evolution of scientific knowledge. This poster presents a research direction comprised of four phases: (i) data cleaning, (ii) concept extraction, (iii) relation learning and (iv) knowledge evolution of academic social tags. For the data cleaning phase, a workflow is presented and evaluated using the Bibsonomy dataset. Future studies will focus on cluster-based outlier detection and topic modeling to extract concepts and derive relations from academic social tags; chronological analysis will be conducted to discover the dynamics of knowledge structure reflected in academic social tags.
Issue Date:2017
Publisher:iSchools
Citation Info:Dong, H., Wang, W., & Coenen, F. (2017). Deriving Dynamic Knowledge from Academic Social Tagging Data: A Novel Research Direction. In iConference 2017 Proceedings (pp. 661-666). https://doi.org/10.9776/17313
Series/Report:iConference 2017 Proceedings
Genre:Conference Poster
Type:Text
Language:English
URI:http://hdl.handle.net/2142/96693
DOI:https://doi.org/10.9776/17313
Rights Information:Copyright 2017 Hang Dong, Wei Wang, and Coenen Frans
Date Available in IDEALS:2017-07-27


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