Abstract
The World Wide Web is a huge collection of web pages where every second, new piece of information is added. Searching and retrieving relevant web resources is a protracted task and finding relevant resources w.r.t. some topic, without any explicit or implicit feedback adds more intricacy to the process. Focused crawling in such scenarios provides a better alternate to generic crawling especially when topic specific or personalized information is required. This paper presents a crawling approach FCHC that uses human cognition for focused crawl on a social bookmarking site initiated with the seeds retrieved from a search engine. It utilizes social bookmark tags as implicit feedback to compute eResource relevance and Vector Space Model to rank the retrieved eResources. A well established metric called harvest ratio is used to compare the results of the proposed approach with the semantic focused crawler and the classic focused crawler. The analysis of the results shows a better performance of social semantic focused crawlers over the semantic and classic focused crawlers.
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Thukral, A., Mendiratta, V., Behl, A., Banati, H., Bedi, P. (2011). FCHC: A Social Semantic Focused Crawler. In: Abraham, A., Lloret Mauri, J., Buford, J.F., Suzuki, J., Thampi, S.M. (eds) Advances in Computing and Communications. ACC 2011. Communications in Computer and Information Science, vol 191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22714-1_29
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DOI: https://doi.org/10.1007/978-3-642-22714-1_29
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