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• W4386775099 abstract "There is a need for strategic ontology integration as the integration of ontologies that are cognitive entities and by themselves are knowledge representation, and description models based on certain logic is essential as the integral structure of the ontology concerning the context and reason it was created must be preserved. So, owing to this, there is a strategic need for automating the strategy of ontology integration, at least up to a certain extent, for that Semantically Inclined Knowledge Centric models and strategies are required as ontologies have to be conceptualized based on human interpretation and cognition and also have to be integrated without any loss or significant modifications concerning the associations between the ontology. In this paper, an ontology integration model for domains such as Mass Communications, Liberal Arts, Media and Journalism, and Public Policy is made use of where the ontologies are specifically generated from distinct heterogeneous document datasets to which the Term Frequency-Inverse Document Frequency (TF-IDF) is applied to identify the rare and yet the most informative terms and for topic modeling, Latent Dirichlet Allocation (LDA) is employed. Furthermore, the classification of the ontologies is achieved using the XGBoost classifier. The strategy involves the extraction of indices from E-books and formalized Taxonomy using the Renyi Entropy with a specific standard deviance measure. The Knowledge Stack achieves entity aggregation formalized using the LOD Cloud, Wiki Data, and Google KG API from that the Semantic Network is formalized. The classified ontologies are the formalized Semantic Network, that is further used to compute the Semantic Similarity under the Tabu search Metaheuristic optimization to formalize, finalize, and integrate the ontologies. The SemantoSim measure, that incorporates differential thresholds, is used in the Tabu search framework to compute semantic similarity. The proposed OISHI framework has achieved an overall Precision of 94.39%, Recall of 96.44%, Accuracy of 95.41%, F-Measure of 95.40%, and the lowest FDR of 0.06." @default.
• W4386775099 created "2023-09-16" @default.
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• W4386775099 date "2023-01-01" @default.
• W4386775099 modified "2023-09-27" @default.
• W4386775099 title "OISHI: An Ontology Integration Framework for Domains of Socio-Humanitarian Importance Incorporating Hybrid Machine Intelligence" @default.
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• W4386775099 doi "https://doi.org/10.1007/978-3-031-43940-7_13" @default.
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