Semantic information retrieval (SIR) approach to retrieve precise information on COVID-19 research
The Semantic Web enhances the current web by enabling machines to understand and interpret data through standardized formats like ontology. Embedding ontologies in the Web allows precise searches, task automation, and improved system interoperability. The proposed approach contributes to semantic in...
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| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | en en en |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2024
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| Subjects: | |
| Online Access: | https://umpir.ump.edu.my/id/eprint/43543/1/final%20sir%20paper.pdf https://umpir.ump.edu.my/id/eprint/43543/2/KSE2024%20e-Programme%20Book%2003112024%20%281%29.pdf https://umpir.ump.edu.my/id/eprint/43543/11/Semantic%20information%20retrieval%20%28SIR%29%20approach%20to%20retrieve%20precise%20information.pdf https://umpir.ump.edu.my/id/eprint/43543/ https://doi.org/10.1109/KSE63888.2024.11063641 |
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| Summary: | The Semantic Web enhances the current web by enabling machines to understand and interpret data through standardized formats like ontology. Embedding ontologies in the Web allows precise searches, task automation, and improved system interoperability. The proposed approach contributes to semantic information retrieval (SIR) for COVID-19 queries using ontology and accurate search query results. After syntactic, semantic and contextual analysis, refined query is formed using ontology-extracted context. The refined query is sent to the search engine to fetch the relevant results. Finally, a ranking module filters and ranks the most relevant result links. The SIR algorithm shows marked improvement in performance for most queries due to semantic analysis and re-ranker module. Sample queries demonstrated 100% precision and 80% recall values for SIR compared to Google. |
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