Files in this item



application/pdf192.pdf (835kB)
(no description provided)PDF


Title:Scientific referential metadata creation with information retrieval and labeled topic modeling
Author(s):Jia, Han; Liu, Xiaozhong
Subject(s):Metadata Generation
Information Retrieval
Referential Metadata
Cyberlearning Resource
Scientific Publication
Labeled Topic Modeling
Abstract:The goal of this research is to propose an innovative method of creating scientific referential metadata for a cyberinfrastructure-enabled learning environment to enhance learning experiences and to help students and scholars obtain better understanding of scientific publications. By using information retrieval, topic modeling, and meta-search approaches, different types of resources, such as related Wikipedia Pages, Datasets, Source Code, Video Lectures, Presentation Slides, and (online) Tutorials, for an assortment of publications and scientific (labeled) topics will be automatically retrieved, associated, and ranked. In order to test our method of automatic cyberlearning referential metadata generation, we designed a user experiment for the quality of the metadata for each scientific keyword and publication and resource ranking algorithms. Evaluation results based on MAP, MRR, and NDCG show that the cyberlearning referential metadata retrieved via meta-search and statistical relevance ranking can effectively help students better understand the essence of scientific keywords and publications.
Issue Date:2013-02
Citation Info:Jia, H., & Liu, X. (2013). Scientific referential metadata creation with information retrieval and labeled topic modeling. iConference 2013 Proceedings (pp. 274-288). doi:10.9776/13192
Genre:Conference Paper / Presentation
Publication Status:published or submitted for publication
Peer Reviewed:is peer reviewed
Rights Information:Copyright © 2013 is held by the authors. Copyright permissions, when appropriate, must be obtained directly from the authors.
Date Available in IDEALS:2013-01-28

This item appears in the following Collection(s)

Item Statistics