• DocumentCode
    234810
  • Title

    Focused crawling with ontology using semi-automatic tagging for relevancy

  • Author

    Gaur, Risha ; Sharma, D.K.

  • Author_Institution
    Comput. Eng. & Applic., GLA Univ., Mathura, India
  • fYear
    2014
  • fDate
    7-9 Aug. 2014
  • Firstpage
    501
  • Lastpage
    506
  • Abstract
    World Wide Web (WWW) is considered to be the most important source of information now a days but it´s difficult to decide which resources are useful and which are more important. Thus to make a specific part of web leading to only the required resources is searched for. The focused crawler crawl a specific part of the web to retrieve the relevant resources. Here in this paper the focused crawler is applied on social network having ontology dependent tags. The ontology here is also used in preprocessing step of focused crawlers to make the search more specific by expanding the search topic semantically. Further the relevancy of manually tagged and semi-automatically tagged resource is compared. Then finally the harvest rate is evaluated for focused crawlers with ontology and using semi-automatic tagging to check for the relevance.
  • Keywords
    Internet; ontologies (artificial intelligence); relevance feedback; search engines; social networking (online); WWW; World Wide Web; focused crawling; ontology dependent tags; relevant resource retrieval; search topic; semiautomatic tagging; social network; Context; Crawlers; Ontologies; Semantics; Tagging; Taxonomy; Web pages; Concept ontology; Crawling; Semantic relevance; Semi-automatic Tags; Topic specific;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Contemporary Computing (IC3), 2014 Seventh International Conference on
  • Conference_Location
    Noida
  • Print_ISBN
    978-1-4799-5172-7
  • Type

    conf

  • DOI
    10.1109/IC3.2014.6897224
  • Filename
    6897224