Semantic Based Intelligent Information Retrieval through Data mining and Ontology

2017 ◽  
Vol 5 (10) ◽  
pp. 210-217
Author(s):  
Muqeem Ahmed ◽  
Keyword(s):  
Data Mining ◽  
Information Retrieval ◽  
Intelligent Information Retrieval ◽  
Intelligent Information

A Layered Parameterized Framework for Intelligent Information Retrieval in Dynamic Social Network using Data Mining

2014 ◽  
pp. 231-248
Author(s):  
Shailendra Kumar Sonkar ◽  
Vishal Bhatnagar ◽  
Rama Krishna Challa
Keyword(s):  
Data Mining ◽  
Information Retrieval ◽  
Social Network ◽  
Age Groups ◽  
Dynamic Social Network ◽  
Intelligent Information Retrieval ◽  
Dynamic Social Networks ◽  
Intelligent Information ◽  
Using Data ◽  
Key Parameter

Dynamic social networks contain vast amounts of data, which is changing continuously. A search in a dynamic social network does not guarantee relevant, filtered, and timely information to the users all the time. There should be some sequential processes to apply some techniques and store the information internally that provides the relevant, filtered, and timely information to the users. In this chapter, the authors categorize the social network users into different age groups and identify the suitable and appropriate parameters, then assign these parameters to the already categorized age groups and propose a layered parameterized framework for intelligent information retrieval in dynamic social network using different techniques of data mining. The primary data mining techniques like clustering group the different groups of social network users based on similarities between key parameter items and by classifying the different classes of social network users based on differences among key parameter items, and it can be association rule mining, which finds the frequent social network users from the available users.

A Layered Parameterized Framework for Intelligent Information Retrieval in Dynamic Social Network using Data Mining

2016 ◽  
pp. 458-475
Author(s):  
Shailendra Kumar Sonkar ◽  
Vishal Bhatnagar ◽  
Rama Krishna Challa
Keyword(s):  
Data Mining ◽  
Information Retrieval ◽  
Social Network ◽  
Age Groups ◽  
Dynamic Social Network ◽  
Intelligent Information Retrieval ◽  
Dynamic Social Networks ◽  
Intelligent Information ◽  
Using Data ◽  
Key Parameter

Dynamic social networks contain vast amounts of data, which is changing continuously. A search in a dynamic social network does not guarantee relevant, filtered, and timely information to the users all the time. There should be some sequential processes to apply some techniques and store the information internally that provides the relevant, filtered, and timely information to the users. In this chapter, the authors categorize the social network users into different age groups and identify the suitable and appropriate parameters, then assign these parameters to the already categorized age groups and propose a layered parameterized framework for intelligent information retrieval in dynamic social network using different techniques of data mining. The primary data mining techniques like clustering group the different groups of social network users based on similarities between key parameter items and by classifying the different classes of social network users based on differences among key parameter items, and it can be association rule mining, which finds the frequent social network users from the available users.

scholarly journals Intelligent Information Retrieval Using Hybrid of Fuzzy Set and Trust

2017 ◽  
Vol 10 (2) ◽  
pp. 311-325
Author(s):  
Suruchi Chawla
Keyword(s):  
Information Retrieval ◽  
Fuzzy Set ◽  
Query Expansion ◽  
Information Need ◽  
Data Set ◽  
Search Results ◽  
Intelligent Information Retrieval ◽  
Main Challenge ◽  
Fuzzy Query ◽  
Intelligent Information

The main challenge for effective web Information Retrieval(IR) is to infer the information need from user’s query and retrieve relevant documents. The precision of search results is low due to vague and imprecise user queries and hence could not retrieve sufficient relevant documents. Fuzzy set based query expansion deals with imprecise and vague queries for inferring user’s information need. Trust based web page recommendations retrieve search results according to the user’s information need. In this paper an algorithm is designed for Intelligent Information Retrieval using hybrid of Fuzzy set and Trust in web query session mining to perform Fuzzy query expansion for inferring user’s information need and trust is used for recommendation of web pages according to the user’s information need. Experiment was performed on the data set collected in domains Academics, Entertainment and Sports and search results confirm the improvement of precision.

scholarly journals Intelligent Information Retrieval Tools for Police

2006 ◽  
pp. 664-665
Author(s):  
Nishant Kumar ◽  
Jan De Beer ◽  
Jan Vanthienen ◽  
Marie-Francine Moens
Keyword(s):  
Information Retrieval ◽  
Intelligent Information Retrieval ◽  
Intelligent Information
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