| dc.description.abstract | With the development of the Internet
in the digital age, operative technologies that
utilize automated tools for searching and
retrieving information in any domain, even those
not on the web, are in great demand. However,
the enormity of the World Wide Web (WWW)
poses a challenge for researchers to retrieve
useful and precise information to meet their
requirements. An Information Retrieval (IR)
system is meant to form a stored knowledge base,
with items accessible to the information seeker.
A major problem of the traditional IR systems is
their inability to provide users with a semantic
description of the knowledge needed by them.
This problem is addressed by this Intelligent
Information Retrieval (IIR), which is capable to
give much more relevant and accurate
information. The need to discover and observe
the real-time mutations in knowledge and
information requires new techniques in the web
IR process. The results of IR contain an
abundance of information that matches with the
queries or searches in varying degrees of
relevance. The relevance of the results is an
important concern and often associates with the
volume of the results: the bigger the volume of
information, the better the relevance, while a
lesser volume of information may have less
relevant content. Seeking solutions for this issue
makes Web IR an active and interesting domain
of research and development. Considering the
past two decades, interest among many has
arisen in software agent technology and its
applications. With Intelligent autonomous agents
being most suitable for numerous applications in
a semantic web environment, many researchers
have proposed different frameworks, which
comprise of details such as information collecting
agents, storing agents, reasoning agents and
querying agents. These structures often take into
consideration semantic web and intelligent
agents research, and other technologies such as
information retrieval and knowledge modeling.
This study focuses on a brief survey of Agentbased
IR Systems on semantic web and ontology.
The performance of such intelligent systems is
calculated by considering the productiveness,
quality of the search and the results obtained,
time performance, and whether users are
satisfied with the search results. | en_US |
