Ontology Based Semantic Enrichment for Improved Information Retrieval Model

Abstract

In the era of vast and heterogeneous information sources, efficient and accurate information retrieval is paramount for users seeking relevant content. This research focuses on enhancing information retrieval systems through the integration of ontology-based semantic enrichment. Traditional keyword-based search methods often fall short of capturing the intricacies of semantic relationships within a given domain, leading to suboptimal retrieval results. To address this limitation, the proposed work leverages ontologies, which represent structured, hierarchical knowledge frameworks defining the relationships among concepts. The proposed approach involves the development and integration of domain-specific ontologies to augment the semantic understanding of textual content. Through the utilization of these ontologies, the information retrieval process is enriched, allowing for a more nuanced and context-aware search experience. Semantic annotations, extracted from the ontologies, enhance the representation of documents, enabling more precise matching of user queries with relevant content. Furthermore, the research explores the integration of machine learning techniques to dynamically adapt and refine the ontological structures over time, ensuring the system's adaptability to evolving domains. The effectiveness of the proposed ontology-based semantic enrichment is evaluated through comprehensive experiments, comparing retrieval performance metrics against traditional methods. The anticipated outcome of this research is an information retrieval system that not only outperforms conventional keyword-based approaches but also provides users with more meaningful and contextually relevant results. The integration of ontology-based semantic enrichment holds promise for advancing the state-of-the-art in information retrieval, contributing to more sophisticated and intelligent search mechanisms in diverse application domains.