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Data Mining (DM) is knowledge-intensive process that can be significantly enhanced by integrating the domain knowledge. Recent research claimed that ontology can play various roles in the DM process. Additionally, ontology can facilitate different steps in the Bayesian Network (BN) construction task. To this end, this paper investigates the advantages of consolidating the Gene Ontology (GO) and the Hierarchical Bayesian Network (HBN) classifier in a flexible framework which preserves the advantages of both ontology and Bayesian theory. The proposed Semantically Aware Hierarchical Bayesian Network (SAHBN) classification model introduces a flexible framework that systematically consolidates domain knowledge in the form of ontology and the DM process. Furthermore, it establishes a solid foundation to explore the possibility of integrating more comprehensive ontological knowledge in the DM process. SAHBN is tested using three datasets in the biomedical domain to predict the effect of the DNA repair gene on the human ageing process. DNA repair genes are classified as either ageing-related or non-ageing related based on their GO biological process terms. Overall, SAHBN classifier shows a very competitive performance compared with the existing Bayesian-based classification algorithms. SAHBN has outperformed existing algorithms in more than 50% of the implemented experiments. Six performance criteria were used to evaluate the performance of the proposed SAHBN model.

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E-Learning has become an essential teaching approach during the COVID-19 pandemic. All over the world, various internet-based learning management systems (Google classroom, Moodle, etc.) were adopted to convey knowledge and enhance learning outcomes. However, measuring learning outcomes and knowledge acquisition in E-Learning environment is a controversial issue. To this end, this paper aims to predict learning outcomes using data mining techniques.  Student data are collected and analyzed to construct the prediction model. The collected data covered students from various undergraduate studies. Cross-Industry Standard Process for Data Mining is used as a research model. The obtained result shows the significant of some attributes in predicting learning outcomes. Four correlation-based attributes selection schemas are applied. The selected attributes are examined using four data mining algorithms: random forest, k-nearest neighbors, Decision Tree, and neural network. The overall performance of the constructed mining models is evaluated using various performance measures: Accuracy, Precision, Recall and F1-score are calculated. Overall, an 86% accuracy is secured.

Conventional Data Mining (DM) algorithms treated data simply as numbers ignoring the semantic relationships among them. Consequently, recent researches claimed that ontology is the best option to represent the domain knowledge for data mining use because of its structural format. Additionally, it is reported that ontology can facilitate different steps in the Bayesian Network (BN) construction task. To this end, this paper investigates the advantages of consolidating the Gene Ontology (GO) and the Hierarchical Bayesian Network (HBN) classifier in a flexible framework, which preserves the advantages of both, ontology and Bayesian theory. The proposed Semantically Aware Hierarchical Bayesian Network (SAHBN) is tested using data set in the biomedical domain. DNA repair genes are classified as either ageing-related or nonageing-related based on their GO biological process terms. Furthermore, the performance of SAHBN was compared against eight conventional classification algorithms. Overall, SAHBN has outperformed existing algorithms in eight experiments out of eleven.