A Semantic Information Retrieval Framework within the Scope of IPS2-PLM

Abramovici, Michael; Gebus, Philip; Göbel, Jens Christian; Dang, Hoang Bao

doi:10.1016/j.procir.2016.03.083

Cited by 4 publications

(1 citation statement)

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“…Manifolds were educated through extricating the objects and supporting the labeling with the administered learning procedure. Concerning the MORS value, a solo class was acknowledged [20]. Regarding the acknowledged semantic class, the upshot was raised.…”

Section: Related Workmentioning

confidence: 99%

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

Ezhilarasi¹,

Kalavathy²

2022

Computer Systems Science and Engineering

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

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Section: Related Workmentioning

confidence: 99%