Semi-Automatic Trend Detection in Scholarly Repository Using Semantic Approach

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).

Authors:

• Fereshteh Mahdavi
• Maizatul Akmar Ismail
• Noorhidawati Abdullah

Keywords:

• Trend
• Semi-Automatic Trend Detection
• Ontology
• Semantic Trend Detection.

References:

[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.