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Self-Adaptive Ontology based Focused Crawler for Social Bookmarking Sites

Author

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  • Aamir Khan

    (GLA University, Mathura, India)

  • Dilip Kumar Sharma

    (GLA University Mathura, India)

Abstract

It is not possible for one person to explore or surf all the relevant websites pre-training to his/her topic. A user might not be able to get the results that he/she expects from the search engine but another user might have some knowledge about some website containing the information about the first user's topical query. Users share their information on a common sharing platform known as SBS (Social Bookmarking Sites). In SBS a user posts a question seeking some knowledge about a certain topic, and then the people who have some knowledge about any website related to the query topic post the URLs of the website. This paper presents a novel method to verify the authenticity and validity of the URL posted in the SBS. The performance of our method is further increased by using a dictionary based learning methodology that finds the contextually similar words that are added to the Ontology.

Suggested Citation

  • Aamir Khan & Dilip Kumar Sharma, 2017. "Self-Adaptive Ontology based Focused Crawler for Social Bookmarking Sites," International Journal of Information Retrieval Research (IJIRR), IGI Global, vol. 7(2), pages 51-67, April.
    • Handle: RePEc:igg:jirr00:v:7:y:2017:i:2:p:51-67
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