Information Retrieval in Intelligent Systems: Current Scenario & Issues

TL;DR

This paper reviews current methods and issues in information retrieval within intelligent systems, highlighting semantic search engines, clustering, and machine learning techniques like Self-organizing maps for improved data visualization and user query handling.

Contribution

It provides an overview of existing tools and techniques such as Swoogle, clustering, and SOM, emphasizing their roles in enhancing web information retrieval.

Findings

Swoogle uses algorithms for semantic web search.

Clustering groups similar web contents effectively.

Self-organizing maps visualize high-dimensional data.

Abstract

Web space is the huge repository of data. Everyday lots of new information get added to this web space. The more the information, more is demand for tools to access that information. Answering users' queries about the online information intelligently is one of the great challenges in information retrieval in intelligent systems. In this paper, we will start with the brief introduction on information retrieval and intelligent systems and explain how swoogle, the semantic search engine, uses its algorithms and techniques to search for the desired contents in the web. We then continue with the clustering technique that is used to group the similar things together and discuss the machine learning technique called Self-organizing maps [6] or SOM, which is a data visualization technique that reduces the dimensions of data through the use of self-organizing neural networks. We then discuss how SOM is used to visualize the contents of the data, by following some lines of algorithm, in the form of maps. So, we could say that websites or machines can be used to retrieve the information that what exactly users want from them.