期刊文献+

A Multi-Threaded Semantic Focused Crawler 被引量:5

A Multi-Threaded Semantic Focused Crawler
原文传递
导出
摘要 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. 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.
出处 《Journal of Computer Science & Technology》 SCIE EI CSCD 2012年第6期1233-1242,共10页 计算机科学技术学报(英文版)
关键词 ELEARNING semantic focused crawler semantically expanded term ONTOLOGY eLearning, semantic focused crawler, semantically expanded term, ontology
  • 相关文献

参考文献49

  • 1Spivack N. Web evolution, http://www.slideshare.net/novaspi- vack/web-evolution-nova-spivack-twine, June 2011.
  • 2Kleinberg J, Lawrence S. The structure of the Web. Science, 2001, 294(5548): 1849-1850.
  • 3Salton G, Buckley C. Term-weighting approaches in auto- matic text retrieval. Information Processing & Management, 1988, 24(5): 513-523.
  • 4Navigli R, Velardi P. An analysis of ontology-based query ex- pansion strategies. In Proc. Workshop on Adaptive Text Ex- traction and Mining, Sept. 2003, pp.42-49.
  • 5Bedi P, Banati H, Thukral A. Social semantic retrieval and ranking of eResources. In Proc. the 2nd Int. Conference on Advances in Recent Technologies in Communication and Computing, Oct. 2010, pp.343-347.
  • 6Berners-Lee T. Giant global graph, http://dig.csaihmit.edu/ breadcrumbs/node/215, May 2011.
  • 7Farber D. From semantic Web (3.0) to the WebOS (4.0). http: //www.zdnet.com/blog/btl/from-semantic-web- 30-to-the-webos-40/4499, May 2011.
  • 8Berners-Lee T, Hendler J, Lassila O. The semantic web. Sci- entific American, 2001 284(3): 34-43.
  • 9Bedi P, Banati H, Thukral A. Use of ontology for reusing web repositories for eLearning. In Technological Develop- ments in Networking, Education and Automation, Elleithy K et at. (eds.), New York, USA: Springer, 2010, pp.97-101.
  • 10Hendler J, Berners-Lee T. From the semantic web to social machines: A research challenge for AI on the World Wide Web. Artificial Intelligence, 2010, 174(2): 156-161.

同被引文献33

引证文献5

二级引证文献15

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部