On the Web and other document repositories the amount of content stored and shared keeps increasing steadily and fast that results in well known difficulties and problems when it comes to finding and properly managing information in massive volumes. In the last decade with the development of search engine technologies Striking progress has been achieved, which collect, store and pre-process information worldwide to return relevant resources instantly in response to users’ needs. However, users still miss or need considerable effort sometimes to reach their targets, even if the sought information is present in the search space. Currently consolidated content description and query processing techniques for Information Retrieval are based on keywords, and therefore provide limited capabilities to grasp and exploit the conceptualizations involved in user needs and content meanings are the common caust. This involves limitations such as the inability to describe relations between search terms to solve the limitations of keyword based , the idea of concept based information retrieval ,conceptual search, understood as searching or retrieving by meanings rather than keyword or literal strings, has been the focus of a wide body of research in the information retrieval field. The semantic technologies such as XML and ontology can play important role for the development of semantic based information retrieval. This paper is an attempt to develop semantic based Information Retrieval for the exploitation of domain knowledge to support semantic information retrieval search capabilities in large document repositories more intelligently; it explores the use of semantic technologies such as xml and ontology to support more expressive queries and more accurate results. for this we have collected the documents from the different domains and design the tree structures of the documents in the form of xml and ontology and data mining technique such as clustering and then retrieve the information from this structure based on user interest that provide the concept based.

Information retrieval,ontology,datamining,semantic web, K-Mean,search

[1] Susan T. Dumais. George W. Furnas. Thomas K. Landaue” Indexing by Latent Semantic Analysis” Bell Communications Research 1990
[2] Gonzalo, Verdejo, Chugur, & Cigarrán “Sense clusters for information retrieval” Proceedings of the COLING/ACL`98 Workshop on Usage of WordNet for NLP, Montreal, 1998
[3] Bruce Croft w.”Boolean queries and term dependencies in probabilistic retrieval” 1986
[4] C. J. van “Information Retrieval”, ACM sigir Forum, v.17 n.4, 1979
[5] Thomas R.Gruber “A Translation Approach to. Portable Ontology Specifications” . Knowledge Acquisition, 5(2):199-220,1993
[6] T. Berners-Lee, J. Hendler, and O. Lassila. “The semantic web”. Scientific American, 284(5):28–37, 2001
[7] OWL Web Ontology Language. http://www.w3.org/TR/owl-ref/.
[8] Blanco, E., Cankaya, H. & Moldovan, D. “Commonsense Knowledge Extraction Using Concepts” Properties. Proceedings of the Twenty-Fourth International Florida Artificial Intelligence Research Society Conference. 2011
[9] Li, H., Tian, Y., Ye, B. & Cai, Q. ” Comparison of Current Semantic Similarity Methods in WordNet”. 201O International Conference on Computer Application and System Modeling (ICCASM 2010). 978-1 -4244-7237-6/10, IEEE.
[10] Nidelkou, E., Papastathis, V., Papadogiorgaki, M., Kompatsiaris, I., Bratu, B., Ribiere, M. & Waddington, S. ” User Profile Modeling and Learning”. In Encyclopedia of Information Science and Technology”, Second Edition. DOI: 10.4018/978-1-60566-026- 4.ch627. 3934-3939. IGI Global.
[11] Harb, H., & Fouad, K.” Semantic web based Approach to learn and update Learner Profile in Adaptive E-Learning.” Al-Azhar Engineering Eleventh International Conference, December 23-26 2010
[12] J. Han and M. Kamber. “Data Mining: Concepts and Techniques”. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2005.
[13] U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth. “From data mining to knowledge discovery in databases”. AI magazine, 17(3):37, 1996.
[14] N. Khasawneh and C.-C. Chan. “Active user-based and ontology-based web log data preprocessing for web usage mining”. In Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence, pages 325–328, 2006.
[15] S. J. Russell and P. Norvig. “Artificial Intelligence: A Modern Approach.” Pearson Education, 2 edition, 2003.
[16] D.Perez-Rey, A. Anguita, and J.Crespo.“Ontodataclean: Ontologybased integration and preprocessing of distributed data. In Biological and Medical Data Analysis”, pages 262–272. Springer, 2006.
[17] N. Balcan, A. Blum, and Y. Mansour.”Exploiting ontology structures and unlabeled data for learning”. In Proceedings of the 30th International Conference on Machine Learning, pages 1112–1120, 2013.
[18] A. Bellandi, B. Furletti, V. Grossi, and A. Romei.” Ontology-driven association rule extraction: A case study”. Contexts and Ontologies Representation and Reasoning, page 10, 2007.
[19] C. Marinica and F. Guillet.”Knowledge-based interactive postmining of association rules using ontologies”. Knowledge and Data Engineering”, IEEE Transactions on, 22(6):784–797, 2010.
[20] G. Mansingh, K.-M. Osei-Bryson, and H. Reichgelt.”Using ontologies to facilitate post-processing of association rules by domain experts”. Information Sciences, 181(3):419–434, 2011.
[21] D. C. Wimalasuriya and D. Dou.”Components for information extraction: Ontology-based information extractors and generic platforms”. In Proceedings of the 19th ACM Conference on Information and Knowledge Management (CIKM), pages 9–18, 2010.
[22] S. J. Russell and P. Norvig. “Artificial Intelligence: A Modern Approach”. Pearson Education, 2 edition, 2003.
[23] T. R. Gruber. “Toward principles for the design of ontologies used for knowledge sharing” International journal of human-computer studies, 43(5):907–928, 1995.
[24] R. Studer, V. R. Benjamins, and D. Fensel.”Knowledge engineering: principles and methods”. Data & knowledge engineering, 25(1)`:161– 197, 1998.
[25] The gene ontology consortium. “Creating the gene ontology resource: design and implementation”. Genome Res., 11(8):1425–1433, August 2001.
[26] D. Lindberg, B. Humphries, and A. McCray.”The Unified Medical Language System”. Methods of Information in Medicine, 32(4):281–291, 1993.
[27] The National Center for Biomedical Ontology. http://www.bioontology.org/.
[28] T. Berners-Lee, J. Hendler, and O. Lassila. “The semantic web”. Scientific American, 284(5):28–37, 2001.
[29] OWL Web Ontology Language. http://www.w3.org/TR/owl-ref/.
[30] N. Balcan, A. Blum, and Y. Mansour.” Exploiting ontology structures and unlabeled data for learning”. In Proceedings of the 30th International Conference on Machine Learning, pages 1112–1120, 2013.
[31] F. Gutierrez, D. Dou, A. Martini, S. Fickas, and H. Zong.” Hybrid ontology-based information extraction for automated text grading”. In Machine Learning and Applications (ICMLA), 2013 12th International Conference on, volume 1, pages 359–364. IEEE, 2013.
[32] [31] D. C. Wimalasuriya and D. Dou.”Ontology-based information extraction:” An introduction and a survey of current approaches”. Journal of Information Science, 36(3):306–323, 2010.