Semantic Recognition of Web Structure to Retrieve Relevant Documents from Google by Formulating Index Term

  • Jinat Ara
  • Hanif BhuiyanEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 924)


Nowadays among various search mechanism, Google is one of the best information retrieval mechanisms for retrieving useful result as per user query. Generally, the complete searching process of Google is completed by crawler and indexing process. Actually, Google performs the documents indexing process considering various features of web documents (title, meta tags, keywords, etc.) which helps to fetches the complementary result by exactly matching the given query with the index term with user interest. Though appropriate indexing process is much difficult but it essential as extracting relevant document completely or partially depend on how much relevant the index term with the document is. However, sometimes, this indexing process is influenced by assorted number of feature of web documents which produced variegated result those are either completely or partially irrelevant to the search and seems unexpected. To reduce this problem, we analyze web documents considering its unstructured (web content and link features) data in terms of efficiency, quality, and relevancy with the user search query and present a keyword-based approach to formulate appropriate index term through semantic analysis using NLP concept. This approach helps to understand the current web structure (effectiveness, quality, and relevancy) and mitigate the current inconsistency problem by generating appropriate, efficient, and relevant index term which might improve the search quality; as satisfied search result is the major concern of Google search engine. The analysis helps to generate appropriate Google web documents index term which might useful to retrieve appropriate and relevant web documents more systematically than other existing approaches (lexicon and web structure based approaches). The experimental result demonstrates that, the proposed approach is an effective and efficient methodology to predict about the competence of web documents and finding appropriate and relevant web documents.


Google Search engine Information retrieval Page ranking algorithm Index term and feature 


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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Jahangirnagar UniversityDhakaBangladesh
  2. 2.University of Asia PacificDhakaBangladesh

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