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Query expansion – Hybrid framework using fuzzy logic and PRF
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NSTL
Elsevier
? 2022 Elsevier LtdSeveral factors hinder information retrieval in the medical profession. Consumers (layman people) often struggle to learn medical terms. Because medical terms are more evident to professionals, it is difficult for consumers to construct a query using medical terms. Consumers would find it easier to access relevant medical information if medical words relevant to their query were automatically added. Various kiosks use approaches using machine vision to form the user queries and monitor their health. This work proposes a hybrid approach to term selection by expanding user queries with medical terms relevant to the medical query. The selection of terms is based on fuzzy similarity reasoning based on two primary term selection strategies. WordNet semantic filtering is applied for preventing query drift, followed by the calculation of BERTScore. The retrieved documents were ranked using Okapi-BM25. For evaluation purposes, six benchmark datasets have been used: CACM, CISI, MEDLINE, ADI, FIRE, and TREC (Covid-19). The results indicate that the suggested technique outperforms the current state-of-the-art.
NSTL
Elsevier
