%0 Journal Article %1 Kaur2020 %A Kaur, Navjot %A Aggarwal, Himanshu %D 2020 %I Springer %J International Journal of Information Technology 2020 %K Artificial Computer Engineering,Vision,general Graphics,Software Imaging,Computer Intelligence,Computer Learning,Pattern Processing Recognition Science,Image Vision,Machine and qe queryexpansion uncovr %P 1-9 %R 10.1007/S41870-020-00464-2 %T Query reformulation approach using domain specific ontology for semantic information retrieval %U https://link.springer.com/article/10.1007/s41870-020-00464-2 %X Conventional search engines provide a lot of irrelevant results to the user’s search query, which consumes a lot of time and effort, as the context and semantics of the request made by the user are not analyzed to the optimum extent. This highlights for the need of a methodology embedded in the search machinery, enabling semantic web search. Ontologies have proven to be an effective technology for such semantic knowledge representation and information retrieval. In this research paper, the researcher has constructed a string ontology in a new domain (the music domain) from scratch using Protégé 5.0 for semantic information retrieval for a query in the search engine. This ontology is further used in the proposed ontology based semantic information retrieval method (OBSIRM), which has been built to refine the web search in the music domain. The researcher has proposed a novel approach to refine the web search in which the query is first replaced with abbreviations along with the use of the multilingual concept to search a query. The results of the proposed method have been compared with Google. The accuracy and efficiency of the proposed method has been measured in terms of precision and recall. The average precision and recall of OBSIRM is 1.43 and 0.7 as compared to generic Google search engine i.e. 1.04 and 0.51. Hence it can safely be concluded that in the domain of research the OBSIRM performs better in terms of users’ expectations than Google (for string instrument domain considered by the researcher) as far as relevant information retrieval is concerned.

@article{Kaur2020, abstract = {Conventional search engines provide a lot of irrelevant results to the user’s search query, which consumes a lot of time and effort, as the context and semantics of the request made by the user are not analyzed to the optimum extent. This highlights for the need of a methodology embedded in the search machinery, enabling semantic web search. Ontologies have proven to be an effective technology for such semantic knowledge representation and information retrieval. In this research paper, the researcher has constructed a string ontology in a new domain (the music domain) from scratch using Protégé 5.0 for semantic information retrieval for a query in the search engine. This ontology is further used in the proposed ontology based semantic information retrieval method (OBSIRM), which has been built to refine the web search in the music domain. The researcher has proposed a novel approach to refine the web search in which the query is first replaced with abbreviations along with the use of the multilingual concept to search a query. The results of the proposed method have been compared with Google. The accuracy and efficiency of the proposed method has been measured in terms of precision and recall. The average precision and recall of OBSIRM is 1.43 and 0.7 as compared to generic Google search engine i.e. 1.04 and 0.51. Hence it can safely be concluded that in the domain of research the OBSIRM performs better in terms of users’ expectations than Google (for string instrument domain considered by the researcher) as far as relevant information retrieval is concerned.}, added-at = {2021-10-15T08:50:32.000+0200}, author = {Kaur, Navjot and Aggarwal, Himanshu}, biburl = {https://www.bibsonomy.org/bibtex/2f39ca0e04011c5e732e105406f06e820/simonha94}, doi = {10.1007/S41870-020-00464-2}, interhash = {e22ba5bfe5353df9697a40ee97c9131c}, intrahash = {f39ca0e04011c5e732e105406f06e820}, issn = {2511-2112}, journal = {International Journal of Information Technology 2020}, keywords = {Artificial Computer Engineering,Vision,general Graphics,Software Imaging,Computer Intelligence,Computer Learning,Pattern Processing Recognition Science,Image Vision,Machine and qe queryexpansion uncovr}, month = {5}, pages = {1-9}, publisher = {Springer}, timestamp = {2021-10-15T08:52:26.000+0200}, title = {Query reformulation approach using domain specific ontology for semantic information retrieval}, url = {https://link.springer.com/article/10.1007/s41870-020-00464-2}, year = 2020 }