Enhanced Neuro-Fuzzy-Based Crop Ontology for Effective Information Retrieval

Ontology is the progression of interpreting conceptions information domain for an assembly handlers. Familiarizing ontology as retrieval (IR) aids in augmenting searching effects user-required relevant information. The crux conventional keyword matching-related IR utilizes advanced algorithms recovering facts from Internet, mapping connection between keywords and information, categorizing outcomes. prevailing procedures consume considerable time, they could not recover proficiently. In this study, through applying a modified neuro-fuzzy algorithm (MNFA), time mitigated, accuracy enhanced trouncing above-stated downsides. proposed method encompasses three phases: i) development crop ontology, ii) implementation system, iii) processing user query. initial phase, developed evaluated by gathering next hash tree constructed using closed frequent patterns (CFPs), MNFA used to train database. last specified query, CFP calculated, similarity assessment results are retrieved performance system measured compared with that existing techniques. Experimental demonstrate has 92.77% simple queries 91.45% complex queries.