Applying semantic similarity measures to enhance topic-specific web crawling

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.

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