Information Retrieval from Heterogeneous Knowledge Sources Based on Multi-agent System

Author(s):  
Tomasz Kogut ◽  
Dominik Ryżko ◽  
Karol Gałązka
2020 ◽  
Vol 10 (1) ◽  
pp. 27-48
Author(s):  
Davy Monticolo ◽  
Inaya Lahoud

Emphasis on knowledge and information is one of the challenges of the 21st century to differentiate the intelligent business enterprises. Enterprises have to develop their organization in order to capture, manage, and use information in a context of continually changing technology. Indeed, knowledge and information are completely distributed in the information network of the company. In addition, knowledge is, by nature, heterogeneous, since it is provided from different information sources like the software, the technical report, the meeting statements, etc. The authors present in this article the architecture of a multi-agent system, which allows the capitalization of the distributed and heterogeneous knowledge. They then present how the agents help business experts to design ontologies in detailing this problem and how the agents extract knowledge from different user databases by using a semantic approach.


Author(s):  
Suruchi Chawla

This chapter explains the multi-agent system for effective information retrieval using information scent in query log mining. The precision of search results is low due to difficult to infer the information need of the small size search query and therefore information need of the user is not satisfied effectively. Information Scent is used for modeling the information need of user web search session and clustering is performed to identify the similar information need sessions. Hyper Link-Induced Topic Search (HITS) is executed on clusters to generate the Hubs and authorities for web page recommendations to users who search with similar intents. This multi-agent system based on clustered query sessions uses query operations like expansion and recommendation to infer the information need of user search queries and recommends Hubs and authorities for effective web search.


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