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Query‐centric scientific topic evolution extraction
Author(s) -
Jensen Scott,
Yu Yingying,
Liu Hans B.,
Liu Xiaozhong
Publication year - 2015
Publication title -
proceedings of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.193
H-Index - 14
ISSN - 2373-9231
DOI - 10.1002/pra2.2015.1450520100127
Subject(s) - computer science , data science , construct (python library) , citation , vocabulary , information retrieval , graph , face (sociological concept) , controlled vocabulary , world wide web , sociology , theoretical computer science , social science , philosophy , linguistics , programming language
Researchers in academia and industry face a deluge of data in our digital world. In this paper, we investigate a novel problem, query‐centric scientific topic evolution. Using heterogeneous graph mining techniques we construct a topic evolution tree (TET) from massive collections of scientific publications, enabling students and researchers to explore the foundation of research topics outside their specialization. Prior research has focused mainly on citation relationships; in this study we employed multiple types of relationships, including authorship, citation, publishing venue, and the contributions authors, papers, and venues have made to a specific topic. We examine multiple restricted meta‐paths in constructing a TET covering topics from the MeSH vocabulary for biomedical research.