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.
The Semantic web is the extension of an existing web that defines a standard by which, information is given in well-defined meaning and enables the machines to understand information. Many kinds of research are going in semantic web space. Researchers follow different approaches to retrieve data from the semantic web. This paper is to investigate the existing situation of the Semantic Web with the focus on effective information retrieval. Based on the architecture of semantic search engine, list of parameters (like ranking algorithm, reasoning mechanism) is framed to carry out a systematic analysis of different techniques proposed by the researchers. This survey identifies around 20 unique models that retrieve the data from the semantic web and information systems. Summary of the selected semantic search models is specified and compared them by means of "classification parameters" defined. This comparison identifies common insight, unique features and also open issues. This study can be used as a guide for future application development and research.
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