Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31100
Semi-Automatic Trend Detection in Scholarly Repository Using Semantic Approach

Authors: Fereshteh Mahdavi, Maizatul Akmar Ismail, Noorhidawati Abdullah


Currently WWW is the first solution for scholars in finding information. But, analyzing and interpreting this volume of information will lead to researchers overload in pursuing their research. Trend detection in scientific publication retrieval systems helps scholars to find relevant, new and popular special areas by visualizing the trend of input topic. However, there are few researches on trend detection in scientific corpora while their proposed models do not appear to be suitable. Previous works lack of an appropriate representation scheme for research topics. This paper describes a method that combines Semantic Web and ontology to support advance search functions such as trend detection in the context of scholarly Semantic Web system (SSWeb).

Keywords: Ontology, trend, Semi-Automatic Trend Detection, Semantic Trend Detection

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1247


[1] Urquhart, S., Larsen, D. (1998) Monitoring For Policy-Relevant Regional Trends Over Time. Ecological Applications, 8.
[2] Box, G. (1976) Time Series Analysis: Forecasting And Control (2nd ed.), Holden-Day, San Francisco
[3] Aleman-Meza, B., Halaschek-Wiener C., Sahoo S (2005) Template Based Semantic Similarity for Security Application.
[4] Ismail M.A, Yaacob, M., Abdul Kareem, S. (2008) Semantic Support Environment for Research Activity. Journal of US-CHINA Education Review, 5, 36-51.
[5] Hoang, L. M. (2006) Emerging Trend Detection from Scientific Online Documents. Japan Advance Institute Of Science and Technology.
[6] Kontostathis, A., Galitsky, L., Pottenger, W., Roy, S. (2003) A Survey of Emerging Trend Detection in Textual Data Mining.
[7] Roy, S., Gevry, D. , Pottenger, W. (2002) Methodologies For Trend Detection In Textual Data Mining.
[8] Lent, B. A., Srikant, R. (1997) Discovering Trends In Text Databases. Third International Conference on Knowledge Discovery and Data Mining. California.
[9] Bun, K. K. (2005) Topic Trend Detection and Mining in World Wide Web. Japanese Society for Artificial Intelligence.
[10] Fukui, K., Saito, K., Kimura, M. , Numao, M (2004) SBSOM: Self- Organizing Map For Visualizing Structure In The Time Series Of Hot Topics. Joint Workshop of Vietnamese Society of AI, SIGKBS-JSAI, ICS-IPSJ, and IEICE-SIGAI on Active Mining.
[11] Shadbolt, N., Hall, W., Lee, B. (2006) The Semantic Web Revisited. IEEE Intelligent Systems.
[12] Ding, L., Finin, T., Joshi, A., (2004) Swoogle: A Search And Metadata Engine For The Semantic Web. 13th ACM International Conference On Information And Knowledge Management.
[13] Guha, R., McCool R.(2003), Semantic Web Testbed. Journal of Web Semantics.
[14] Kiryakov, A., Popov, B., Terziev, I. (2005) Semantic Annotation, Indexing, and Retrieval, Elsevier's Journal of Web Semantics.
[15] Harmelen, F., Antoniou, G. (2008) A Semantic Web Primer.