Ontology Based Information Retrieval By Using Semantic Query

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.