The volume of data is increasing quickly in the modern day. Effective
information retrieval techniques are needed to extract important facts from such a large
collection of information. As a result, retrieval of information is the process of
gathering valid data from a variety of sources. The majority of the time, information is
retrieved from the internet using search queries. The aim of this research is to explore
various issues existing in information retrieval techniques and to propose new
techniques to overcome existing challenges in the field of Information retrieval.
Modern information retrieval methods have been examined, and it was discovered that
they do not take semantic keyword knowledge into account when returning results. The
semantic web is a development of the internet that enables computers to comprehend
human inquiries in terms of their intent and produce pertinent responses.
This research mainly focuses on Ontology-Based Information Retrieval which can
support semantic similarity and retain the view of an approximate search in a document
repository using machine learning techniques. Further, this research works explores an
adaptive update model for retrieving the information and proposes a semantic search
model for the given user query. The objective of ontology-based semantic web
information search is to increase the accuracy, precision and recall of user queries.
Keywords: Information retrieval, Machine learning, Ontology, Semantic web, Semantic query expansion.
