A Novel Architecture of Perception Oriented Web Search Engine Based on Decision Theory

The number of active web pages increases exponentially. According to the survey, the web has 14.3 trillion active web pages. The problem faced by present search engines is difficulty in returning relevant information. The current search engines do not perform semantic search and are not capable to return results based on user's perception. In this paper a perception based search engine is proposed that returns results as per the user point of view. To achieve semantic searching, a knowledge base is constructed which stores knowledge in the form of predicates. To extract knowledge from knowledge base, decision theory is used that does not restrict to any specific domain.

[1]  Satoshi Sekine,et al.  The Domain Dependence of Parsing , 1997, ANLP.

[2]  Carol Peters,et al.  Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access , 2008 .

[3]  Lailatul Qadri Zakaria,et al.  A semantic retrieval of web documents using domain ontology , 2005, Int. J. Web Grid Serv..

[4]  Gerard Salton,et al.  The SMART Retrieval System—Experiments in Automatic Document Processing , 1971 .

[5]  Dragomir R. Radev,et al.  Novel Methods in Information Retrieval , 2008 .

[6]  Jade Goldstein-Stewart,et al.  The use of MMR, diversity-based reranking for reordering documents and producing summaries , 1998, SIGIR '98.

[7]  J. J. Rocchio,et al.  Relevance feedback in information retrieval , 1971 .

[8]  Chris Buckley,et al.  Implementation of the SMART Information Retrieval System , 1985 .

[9]  James P. Callan,et al.  The effectiveness of query expansion for distributed information retrieval , 2001, CIKM '01.

[10]  Han-Bing Yan,et al.  A New Probabilistic Model for Bayes Document Classification , 2013 .

[12]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[13]  Djoerd Hiemstra,et al.  On the Evaluation of Snippet Selection for Information Retrieval , 2008, CLEF.

[14]  S. Russel and P. Norvig,et al.  “Artificial Intelligence – A Modern Approach”, Second Edition, Pearson Education, 2003. , 2015 .

[15]  C. Hepburn.  Information retrieval system. , 1978, Journal of clinical orthodontics : JCO.

[16]  Rahul Rishi,et al.  Semantic Structure Representation of HTML Document Suitable for Semantic Document Retrieval , 2012 .

[17]  Stephen E. Robertson,et al.  Selecting good expansion terms for pseudo-relevance feedback , 2008, SIGIR '08.