A Personalized Intelligent Web Retrieval System Based on the Knowledge-Base Concept and Latent Semantic Indexing Model

2009 ◽  
Author(s):  
Lihua Wu ◽  
JianPing Feng ◽  
Yunfen Luo
Keyword(s):  
Knowledge Base ◽  
Retrieval System ◽  
Latent Semantic Indexing ◽  
Semantic Indexing ◽  
Web Retrieval ◽  
Base Concept ◽  
Indexing Model

scholarly journals Formalization of Technological Knowledge in the Field of Metallurgy using Document Classification Tools Supported with Semantic Techniques

2017 ◽  
Vol 62 (2) ◽  
pp. 715-720 ◽  
Author(s):  
K. Regulski
Keyword(s):  
Knowledge Base ◽  
Latent Semantic Indexing ◽  
Semantic Knowledge ◽  
Technological Knowledge ◽  
Semantic Integration ◽  
Semantic Indexing ◽  
Text Documents ◽  
Semantic Techniques ◽  
Semantic Knowledge Base

AbstractThe process of knowledge formalization is an essential part of decision support systems development. Creating a technological knowledge base in the field of metallurgy encountered problems in acquisition and codifying reusable computer artifacts based on text documents. The aim of the work was to adapt the algorithms for classification of documents and to develop a method of semantic integration of a created repository. Author used artificial intelligence tools: latent semantic indexing, rough sets, association rules learning and ontologies as a tool for integration. The developed methodology allowed for the creation of semantic knowledge base on the basis of documents in natural language in the field of metallurgy.

An Information Retrieval Model Based on Latent Semantic Indexing with Intelligent Preprocessing

2005 ◽  
Vol 04 (04) ◽  
pp. 279-285 ◽  
Author(s):  
Ch. AswaniKumar ◽  
Ankush Gupta ◽  
Mahmooda Batool ◽  
Shagun Trehan
Keyword(s):  
Information Retrieval ◽  
Retrieval System ◽  
Latent Semantic Indexing ◽  
Information Retrieval System ◽  
Semantic Indexing ◽  
Human Intervention ◽  
Retrieval Model ◽  
User Query ◽  
Removal Processes ◽  
Current Area

The primary goal of an information retrieval system is to retrieve all the documents that are relevant to the user query. Disparities between the vocabulary of the system's authors and that of their users pose difficulties when information is processed without human intervention. Preprocessing the documents and user queries using intelligence techniques to remove the ambiguities in representation and indexing is the current area of research. In this paper, we present a novel intelligent method that has been appended to existing stemming and stopword removal processes. We designed an information retrieval system based on the proposed method using latent semantic indexing. The experimental results of the system using the proposed method exhibits the superiority over other systems based on traditional preprocessing methods.

Analyzing Large-Scale Proteomics Projects with Latent Semantic Indexing

2008 ◽  
Vol 7 (1) ◽  
pp. 182-191 ◽  
Author(s):  
Sebastian Klie ◽  
Lennart Martens ◽  
Juan Antonio Vizcaíno ◽  
Richard Côté ◽  
Phil Jones ◽  
...  
Keyword(s):  
Large Scale ◽  
Latent Semantic Indexing ◽  
Semantic Indexing
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