Text based Semantic information predictions using user behavior

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2017 by IJETT Journal
Volume-45 Number-10
Year of Publication : 2017
Authors : Sonali Pawar
DOI :  10.14445/22315381/IJETT-V45P298


Sonali Pawar "Text based Semantic information predictions using user behavior", International Journal of Engineering Trends and Technology (IJETT), V45(10),521-523 March 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

For Searching and managing online growth of information is becoming a difficult task. The major challenge is to improve users search experience. The current technique that is involved in Content description and query processing in Information Retrieval (IR) are based on keywords. I am therefore trying to improve the quality of search results. In this paper I am trying to optimize the search engines results. Mostly used search engines are Google, Yahoo and Bing. Thus the query q is provided as an input to search engine followed by retrieving relevant ddocuments/ links to user. Depending upon the user behavior the documents are retrieved to user. For this we will firstly create a login section where user will provide interests, hobbies and designation in it, to make searching more useful.


[1] Roufan Wang, Shan Jiang and Yan Zhang, “Re-ranking Search Results Using Semantic Similarity”, Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp.1047-1051, 2011.
[2] Sanjeev Patel, Kriti Khanna and Vishnu Sharma, “Documents Ranking Using New Learning Approach”, International Conference on Computing Communication and Automation (ICCCA), pp. 65-70, 2016.
[3] Poonam Chahal, Manjeet Singh and Suresh Kumar “Ranking of Web Documents using Semantic Similarity”, International Conference on Information Systems and Computer Networks (ISCON), pp.145-150, 2013.
[4] Abdelkrim Bouramoul, Mohamed-Khireddine Kholladi and Bich- Lien Doan, “An ontology-based approach for semantics ranking of the web search engines results” , International Conference on Multimedia Computing and Systems (ICMCS), 2012.
[5] Ashlesha Gupta, Ashutosh Dixit and A. K. Sharma, “Relevant Document Crawling with Usage Pattern and Domain Profile Based Page Ranking”, International Conference on Information Systems and Computer Networks , pp.119-124, 2013.
[6] Chunchen Liu and Jianqiang Li, “Semantic-based Composite Document Ranking”, IEEE Sixth International Conference on Semantic Computing, pp.126-129, 2012.
[7] Shashi Shekhar, K. V. Arya, Rohit Agrawal and Rakesh Kumar, “A WEBIR Crawling Framework for Retrieving Highly Relevant Web Documents: Evaluation Based on Rank Aggregation and Result Merging Algorithms”, International Conference on Computational Intelligence and Communication Systems, pp.83-88, 2011.
[8] Ching-Yang Tseng, ChangChun Lu and Cheng-Fu Chou, “Efficient Privacy-Preserving Multi-keyword Ranked Search Utilizing Document Replication and Partition”, 12th Annual IEEE Consumer Communications and Networking Conference (CCNC), pp.671-676, 2015.
[9] Kozhushko O.A. and Tarkov M.S. “Using Hierarchical Temporal Memory for Document Ranking System Identification”, International Siberian Conference on Control and Communications (SIBCON), 2015.
[10] Syandra Sari and Mima Adriani, “Learning to rank for determining relevant document in Indonesian-English cross language information retrieval using BM25” International Conference on Advanced Computer Science and Information Systems (ICACSIS), pp.309-314, 2014.
[11] Ajni.K.Ajai and R.S. Rajesh, “Hierarchical Multi-keyword Ranked Search for Secured Document Retrieval in Public Clouds”, International Conference on Communication and Network Technologies (ICCNT), pp.33-37, 2014.
[12] R.Sivashankari and Dr. B.Valarmathi, “An Empirical Semi- Supervised Machine Learning Approach on Extracting and Ranking Document Level Multi-Word Product Names Using Improved C-value Approach”, International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp.770-775, 2016.
[13] Azam Feyznia, Mohsen Kahani and Reza Ramezani, “A Link Analysis Based Ranking Algorithm for Semantic Web Documents”, 6th Conference on Information and Knowledge Technology (lKT ), pp.123-127,2014.
[14] Veningston .K and Dr.R.Shanmugalakshmi, “Enhancing personalized web search re-ranking algorithm by incorporating user profile”, Third International Conference Computing Communication & Networking Technologies (ICCCNT), 2012.
[15] Patel Jay, Pinal Shah, Kamlesh Makvana and Parth Shah, “Review on web search personalization through semantic data”, International Conference on Electrical, Computer and Communication Technologies (ICECCT), 2015.
[16] Ahmad Hawalah and Maria Fasli, “A Hybrid Re-ranking Algorithm Based on Ontological User Profiles”, 3rd Computer Science and Electronic Engineering Conference (CEEC), pp.50-55, 2011.

Content description, keywords, Information Retrieval, Search engine, Query processing.