Ontology-based soft computing and machine learning model for efficient retrieval

Unstructured and unorganized data always degrade the performance of search techniques and produce irrelevant results in response to the query as well as decrease the speed of retrieval results. Ontology in semantic web (SW) provides an adequate solution to represent the knowledge, because of its bac...

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Bibliographic Details
Published in:Knowledge and information systems 2024-02, Vol.66 (2), p.1371-1402
Main Authors: Anand, Sanjay Kumar, Kumar, Suresh
Format: Article
Language:English
Subjects:
Bayesian analysis
Clustering
Computer Science
Data Mining and Knowledge Discovery
Database Management
Decision trees
Information retrieval
Information Storage and Retrieval
Information Systems and Communication Service
Information Systems Applications (incl.Internet)
IT in Business
Knowledge representation
Machine learning
Ontology
Performance degradation
Performance evaluation
Regular Paper
Semantic web
Soft computing
Statistical analysis
Support vector machines
Uncertainty
Unstructured data
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Summary:Unstructured and unorganized data always degrade the performance of search techniques and produce irrelevant results in response to the query as well as decrease the speed of retrieval results. Ontology in semantic web (SW) provides an adequate solution to represent the knowledge, because of its backbone knowledge of an application or domain. But, domain ontology has three basic problems while retrieving useful knowledge from a domain ontology: (a) structuring/arrangement, (b) unnecessary knowledge reduction, selection and extraction, and (c) speeding up the retrieval process. To resolve these problems, we proposed multi-level k-mean clustering approach with rough set and Bayesian network model for ontology (MLK-rBO). The proposed model works in four different phases—clustering, knowledge discovery, building a probabilistic network, and model evaluation. The model ensembles three different techniques, namely clustering, rough set (RS), and Bayesian network (BN). Finally, the proposed model is tested with statistical parameters and compared with other models, namely decision tree (DT), random forest (RF), and support vector machine (SVM) to evaluate performance. By analyzing experimental results, we observed that the MLK-rBO gives better accuracy: 98.36% for survey data (fever) and 86% for Wine quality data than available models.
ISSN:0219-1377
0219-3116
DOI:10.1007/s10115-023-01990-8