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Temporal analysis on pairs of classified index terms of literature databases Ma, Zheng; Weihe, Karsten Sammelbandbeitrag | Aus: Markscheffel, Bernd;Fischer, Daniel;Büttner, Daniela;Kretschmer, Hildrun (Hrsg.): Collaboration - changing the global landscape of science: Proceedings of 10th International Conference on Webometrics, Informetrics and Scientometrics & 15th COLLNET Meeting 2014 | Ilmenau: Digital Bibliothek Thüringen | 2014 35058 Endnote: Autor*innen: Ma, Zheng; Weihe, Karsten
Titel: Temporal analysis on pairs of classified index terms of literature databases
Aus: Markscheffel, Bernd;Fischer, Daniel;Büttner, Daniela;Kretschmer, Hildrun (Hrsg.): Collaboration - changing the global landscape of science: Proceedings of 10th International Conference on Webometrics, Informetrics and Scientometrics & 15th COLLNET Meeting 2014, Ilmenau: Digital Bibliothek Thüringen, 2014 , S. 457-464
URN: urn:nbn:de:gbv:ilm1-2014200150
URL: http://www.db-thueringen.de/servlets/DocumentServlet?id=25162
Dokumenttyp: 4. Beiträge in Sammelbänden; Tagungsband/Konferenzbeitrag/Proceedings
Sprache: Englisch
Schlagwörter: Computerlinguistik; Erziehungswissenschaft; Häufigkeit; Indexierung; Inhaltanalyse; Text; Veröffentlichung; Wissen; Wissenschaft
Abstract: Trend analysis and anomaly detection (Chan & Mahoney, 2005; Wei, Kumar, Lolla, & Keogh, 2005) is gaining more and more interest since more than a decade. It gains focus in the new era of web2.0. As part of the project Knowledge Discovery in Scientific Literature, we developed new promising perspectives to detect trend and other interesting temporal patterns of index terms in the literature databases of educational domain. More specifically, we assign categories to index terms and investigate the index term pairs of special interest. We designed several measures to capture the characteristics of the evolution of individual index terms and pairs of index terms. Result shows our methodology is effective to find interesting temporal patterns, e.g. dependency relationship between index terms and helpful to detect trend. (DIPF/Orig.)
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