Version 1
: Received: 5 January 2024 / Approved: 8 January 2024 / Online: 8 January 2024 (13:33:45 CET)
How to cite:
Javed, A.; Ashraf, H.; Jhanjhi, N. Query Reformulation Approach Using Domain Specific Ontology. Preprints2024, 2024010585. https://doi.org/10.20944/preprints202401.0585.v1
Javed, A.; Ashraf, H.; Jhanjhi, N. Query Reformulation Approach Using Domain Specific Ontology. Preprints 2024, 2024010585. https://doi.org/10.20944/preprints202401.0585.v1
Javed, A.; Ashraf, H.; Jhanjhi, N. Query Reformulation Approach Using Domain Specific Ontology. Preprints2024, 2024010585. https://doi.org/10.20944/preprints202401.0585.v1
APA Style
Javed, A., Ashraf, H., & Jhanjhi, N. (2024). Query Reformulation Approach Using Domain Specific Ontology. Preprints. https://doi.org/10.20944/preprints202401.0585.v1
Chicago/Turabian Style
Javed, A., Humaira Ashraf and NZ Jhanjhi. 2024 "Query Reformulation Approach Using Domain Specific Ontology" Preprints. https://doi.org/10.20944/preprints202401.0585.v1
Abstract
Query reformulation is a fundamental task in information retrieval systems aimed at improving the accuracy and relevance of search results. This abstract presents a query reformulation approach that utilizes a domain-specific ontology to enhance the effectiveness of query reformulation. The use of ontology enables a more precise understanding of the domain and its associated concepts, leading to more accurate query interpretation and reformulation. The proposed approach consists of two main steps: ontology-based query interpretation and ontology-guided query expansion. The proposed approach use Power thesaurus tool that perform sentiment analysis for suggestion of the synonyms of the query words to improve the accuracy of the retrieved results. The proposed methodology gives high accuracy as compared to the previous approaches that used Wordnet for the suggestions of synonyms. The proposed approach demonstrates the potential of utilizing ontologies in query reformulation, offering a more accurate and context-aware search experience in domain-specific information retrieval systems.
Keywords
Information Retrieval; Ontology; Machine Learning
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
