Ranking Documents Based on the Semantic Relations Using Analytical Hierarchy Process: Query Expansion and Ranking Process

With the rapid growth of the World Wide Web comes the need for a fast and accurate way to reach the information required. Search engines play an important role in retrieving the required information for users. Ranking algorithms are an important step in search engines so that the user could retrieve the pages most relevant to his query. In this work, we present a method for utilizing genealogical information from ontology to find the suitable hierarchical concepts for query extension, and ranking web pages based on semantic relations of the hierarchical concepts related to query terms, taking into consideration the hierarchical relations of domain searched (sibling, synonyms and hyponyms) by different weighting based on AHP method. So, it provides an accurate solution for ranking documents when compared to the three common methods. Keywords—Semantic rank; ranking web; ontology; search engine; information retrieval

[1]  Simone Paolo Ponzetto,et al.  WikiRelate! Computing Semantic Relatedness Using Wikipedia , 2006, AAAI.

[2]  N. Bhushan,et al.  Strategic Decision Making: Applying the Analytic Hierarchy Process , 2004 .

[3]  Hiromitsu Hama,et al.  Optimal Crawling Strategies for Multimedia Search Engines , 2009, 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing.

[4]  Komal Kumar Bhatia,et al.  Page Ranking Algorithms: A Survey , 2009, 2009 IEEE International Advance Computing Conference.

[5]  Anjali Awasthi,et al.  Using AHP and Dempster-Shafer theory for evaluating sustainable transport solutions , 2011, Environ. Model. Softw..

[6]  Mayank Singh,et al.  Ontology Based Information Retrieval in Semantic Web: A Survey , 2013 .

[7]  Yong Tang,et al.  EIIS: An Educational Information Intelligent Search Engine Supported by Semantic Services , 2011, Int. J. Distance Educ. Technol..

[8]  Roy Rada,et al.  Development and application of a metric on semantic nets , 1989, IEEE Trans. Syst. Man Cybern..

[9]  Sukrati Pathak,et al.  A New Web Document Retrieval Method Using Extended-IOWA (Extended-Induced Ordered Weighted Averaging) Operator on HTML Tags , 2014 .

[10]  Alexandros Potamianos,et al.  Unsupervised Semantic Similarity Computation between Terms Using Web Documents , 2010, IEEE Transactions on Knowledge and Data Engineering.

[11]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[12]  Zahid Hasan.  Research Challenges in Web Data Mining , 2012 .

[13]  Bing Han,et al.  TimeRank: A method of improving ranking scores by visited time , 2008, 2008 International Conference on Machine Learning and Cybernetics.

[14]  Youssef Bassil,et al.  Semantic-Sensitive Web Information Retrieval Model for HTML Documents , 2012, ArXiv.

[15]  Mario Piattini,et al.  Towards an ontology for global software development , 2012, IET Softw..

[16]  Neelam Tyagi.  Weighted Page Rank Algorithm Based on Number of Visits of Links of Web Page , 2012 .

[17]  R. H. Goudar,et al.  Web page indexing through page ranking for effective semantic search , 2013, 2013 7th International Conference on Intelligent Systems and Control (ISCO).

[18]  Christiane Fellbaum,et al.  Combining Local Context and Wordnet Similarity for Word Sense Identification , 1998 .

[19]  Gilad Mishne,et al.  ClickRank: Learning Session-Context Models to Enrich Web Search Ranking , 2012, TWEB.

[20]  J. Jayanthi,et al.  An Integrated Page Ranking Algorithm for Personalized Web Search , 2011 .

[21]  Angelos Hliaoutakis,et al.  Semantic Similarity Measures in MeSH Ontology and their application to Information Retrieval on Medline , 2005 .

[22]  ChengXiang Zhai,et al.  Positional relevance model for pseudo-relevance feedback , 2010, SIGIR.

[23]  David W. Conrath,et al.  Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy , 1997, ROCLING/IJCLCLP.

[24]  Eyas El-Qawasmeh,et al.  Ranking web sites using domain ontology concepts , 2010, Inf. Manag..

[25]  Yajun Du,et al.  Semantic ranking of web pages based on formal concept analysis , 2013, J. Syst. Softw..

[26]  Carlos Castillo,et al.  Effective web crawling , 2005, SIGF.

[27]  Li Ning,et al.  Using Information Content to Evaluate Semantic Similarity on HowNet , 2012, CIS 2012.

[28]  Ricardo A. Baeza-Yates,et al.  Web page ranking using link attributes , 2004, WWW Alt. '04.

[29]  Xiaoyong Du,et al.  CoRE: A Context-Aware Relation Extraction Method for Relation Completion , 2013, IEEE Transactions on Knowledge and Data Engineering.

[30]  Sankar K. Pal,et al.  Web mining in soft computing framework: relevance, state of the art and future directions , 2002, IEEE Trans. Neural Networks.

[31]  Furu Wei,et al.  Exploring hypergraph-based semi-supervised ranking for query-oriented summarization , 2013, Inf. Sci..

[32]  Sung-Bae Cho,et al.  Personalized mining of web documents using link structures and fuzzy concept networks , 2007, Appl. Soft Comput..

[33]  Zhenyu Liu,et al.  Textual Document Indexing and Retrieval via Knowledge Sources and Data Mining , 2002 .

[34]  Lihui Chen,et al.  Web documents categorization using fuzzy representation and HAC , 2000, Proceedings of the First International Conference on Web Information Systems Engineering.

[35]  Hinrich Schütze,et al.  Introduction to information retrieval , 2008 .

[36]  Dekang Lin,et al.  An Information-Theoretic Definition of Similarity , 1998, ICML.

[37]  Martha Palmer,et al.  Verb Semantics and Lexical Selection , 1994, ACL.

[38]  Graeme Hirst,et al.  Evaluating WordNet-based Measures of Lexical Semantic Relatedness , 2006, CL.