Ontology based Semantic Information Retrieval System using Data Ranking

Information retrieval is the process of searching through a database for material that meets a specific request. Due to the tremendous rise in popularity of knowledge discovery apps, end users are increasingly expected to craft sophisticated database search queries in order to access relevant data. Semantic information retrieval uses semantic analysis to return results that are more directly related to the user's original query. When this happens, ontology-based knowledge representation can be more efficient than other methods of representation like semantic networks and frames at facilitating semantic retrieval. Users in this category are required to understand not just the semantic connections between data as well as the structural complexity of complex databases. To get around these problems, Researchers have been focusing on enhancing the relation between data and search requests to provide outcomes that are more in line with users' research objectives to avoid these issues. As a result of this, ontologies are being used more and more for knowledge representation and interactive query creation. Considerations for ontology modelling, processing, and the translation of ontological knowledge into database search queries are all addressed in suggested ontology-based information retrieval strategy. Our findings show that the algorithm achieves state-of-the-art performance on a variety of measures of semantic information retrieval efficacy, including precision, recall, and f-measure. It has been shown through experiments that the algorithm speeds up the process of semantic information retrieval and enhances the system's capacity for expressing knowledge.