The Web comprises of voluminous rich learning content. The volume of ever growing learning resources however leads to the problem of information overload. A large number of irrelevant search results generated from sea...The Web comprises of voluminous rich learning content. The volume of ever growing learning resources however leads to the problem of information overload. A large number of irrelevant search results generated from search engines based on keyword matching techniques further augment the problem. A learner in such a scenario needs semantically matched learning resources as the search results. Keeping in view the volume of content and significance of semantic knowledge, our paper proposes a multi-threaded semantic focused crawler （SFC） specially designed and implemented to crawl on the WWW for educational learning content. The proposed SFC utilizes domain ontology to expand a topic term and a set of seed URLs to initiate the crawl. The results obtained by multiple iterations of the crawl on various topics are shown and compared with the results obtained by executing an open source crawler on the similar dataset. The results are evaluated using Semantic Similarity, a vector space model based metric, and the harvest ratio.展开更多
文摘The Web comprises of voluminous rich learning content. The volume of ever growing learning resources however leads to the problem of information overload. A large number of irrelevant search results generated from search engines based on keyword matching techniques further augment the problem. A learner in such a scenario needs semantically matched learning resources as the search results. Keeping in view the volume of content and significance of semantic knowledge, our paper proposes a multi-threaded semantic focused crawler （SFC） specially designed and implemented to crawl on the WWW for educational learning content. The proposed SFC utilizes domain ontology to expand a topic term and a set of seed URLs to initiate the crawl. The results obtained by multiple iterations of the crawl on various topics are shown and compared with the results obtained by executing an open source crawler on the similar dataset. The results are evaluated using Semantic Similarity, a vector space model based metric, and the harvest ratio.
