Semantic Based Re-ranking Model Using WordNet for Retrieving Web URL in E-learning

Subashka Ramesh; Dr.A.Chandrasekar

Abstract

E-learning is fast, appropriate and just-in-time learning growth from the learning requirements of the new dynamically changing, distributed business world. Nowadays modeling user’s preferences is one of the tricky tasks in elearning systems that deal with huge volumes of information. The term “Semantic Web” encompasses efforts to build a new search engine that supports content with formal semantics, which Permits better potentialities for browsing and exploring through the cyberspace. Information retrieval by searching users query on the web is not a fresh idea but has different challenges when it compared to general information retrieval. Different search engine return different search results due to variation in indexing and search process. Semantic web can solve the problem in web with semantic annotations to provide intelligent and meaningful understanding by way of making use of query interface mechanism. In this work, we present a method for using genealogical data from ontology in finding the compatible hierarchical principles for question extension, and ranking websites founded on semantic family members of the hierarchical principles related to question terms, thinking of the hierarchical members of the family of domain searched (sibling, synonyms and hyponyms) via extraordinary weighting based on Re-ranking method. So, it provides an accurate answer for ranking records when compared to the previous methods. Keywords: Semantic Web, Re-ranking,

Keywords

Tokenizer, Stopword Elimination, Parser

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Cites this article as

S. Ramesh, Dr.A.Chandrasekar, "Semantic Based Re-ranking Model Using WordNet for Retrieving Web URL in E-learning", International Journal of Innovative Research in Engineering & Management (IJIREM), Vol-3, Issue-6, Page No-503-507, 2016. Available from: