Contextual proximity based term-weighting for improved web information retrieval

  • Authors:

  • M. P. S. Bhatia;Akshi Kumar Khalid

  • Affiliations:

  • Netaji Subhas Institute of Technology, University of Delhi, India;Netaji Subhas Institute of Technology, University of Delhi, India

  • Venue:
  • KSEM'07 Proceedings of the 2nd international conference on Knowledge science, engineering and management
  • Year:
  • 2007

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Abstract

Despite its success as a preferred or de-facto source of information, the Web implicates two key challenges: To provide improved systems that retrieve the most relevant information available, and, secondly, how to target search on information that satisfies user's need with accurate balance of novelty and relevance. Nevertheless, Web content is not always easy to use. Due to the unstructured and semi-structured nature of the Web pages & design idiosyncrasy of Websites, it is a challenging task to organize & manage content from the Web. Web Mining tries to solve these issues that arise due to the WWW phenomenon. This paper proposes a novel context-based paradigm for improving Web Information Retrieval, given a multi-term query. The technique referred to as the Contextual Proximity Model (CPM), captures query context and matches it against term context in documents to determine term significance and topical relevance. It makes use of the co-information metric to detect the query context. This contextual evidence is used as an additional input to disambiguate and augment the user's explicit query and dynamically contribute to the term frequency metric to ensure a vital, positive impact on retrieval accuracy.