Applying semantic similarity measures to enhance topic-specific web crawling


Pesaranghader, Ahmad and Pesaranghader, Ali and Mustapha, Norwati (2013) Applying semantic similarity measures to enhance topic-specific web crawling. In: 2013 13th International Conference on Intelligent Systems Design and Applications (ISDA). IEEE, pp. 205-212. ISBN 978-1-4799-3515-4

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As the Internet grows rapidly, finding desirable information becomes a tedious and time consuming task. Topic-specific web crawlers, as utopian solutions, tackle this issue through traversing the Web and collecting information related to the topic of interest. In this regard, various methods are proposed. Nevertheless, they hardly consider desired sense of the given topic which would certainly play an important role to find relevant web pages. In this paper, we attempt to improve topic-specific web crawling by disambiguating the sense of the topic. This would avoid crawling irrelevant links interlaced with other senses of the topic. For this purpose, by considering links hypertext semantic, we employ Lin semantic similarity measure in our crawler, named LinCrawler, to distinguish topic sense-related links from the others. Moreover, we compare LinCrawler against TFCrawler which only considers frequency of terms in hypertexts. Experimental results show LinCrawler outperforms TFCrawler to collect more relevant web pages.

Item Type: Book Section
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Creative Multimedia (FCM)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 06 Nov 2014 04:59
Last Modified: 06 Nov 2014 04:59


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