Data Quality Assessment and Recommendation of Feature Selection Algorithms: An Ontological Approach


  • Aparna Nayak SFI Centre for Research Training in Machine Learning, School of Computer Science, Technological University Dublin, Dublin, Republic of Ireland
  • Bojan Božić SFI Centre for Research Training in Machine Learning, School of Computer Science, Technological University Dublin, Dublin, Republic of Ireland
  • Luca Longo SFI Centre for Research Training in Machine Learning, School of Computer Science, Technological University Dublin, Dublin, Republic of Ireland



Data quality, feature selection algorithm, meta-features, ontology, recommendation


Feature selection plays an important role in machine learning and data mining problems. Identifying the best feature selection algorithm that helps to remove irrelevant and redundant features is a complex task. This research tries to address it by recommending a feature selection algorithm based on dataset meta-features. The main contribution of the work is the use of Semantic Web principles to develop a recommendation model for the feature selection algorithm. As a result, dataset meta-features are modeled in a domain ontology, and a set of Semantic Web rule language (SWRL) predictive rules have been proposed to recommend a feature selection algorithm. The result of this research is a feature selection algorithm recommendation based on the data characteristics and quality (FSDCQ) ontology, which not only helps with recommendations but also finds the data points with data quality violations. An experiment is conducted on the classification datasets from the UCI repository to evaluate the proposed ontology. The usefulness and effectiveness of the proposed method is evaluated by comparing it with the widely used method in the literature for the recommendation. Results show that the ontology-based recommendations are equally good as the widely used recommendation model, which is k-NN, with added benefits.


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Author Biographies

Aparna Nayak, SFI Centre for Research Training in Machine Learning, School of Computer Science, Technological University Dublin, Dublin, Republic of Ireland

Aparna Nayak received her M.Tech degree from Manipal Academy of Higher Education, India. She has more than seven years of teaching experience. She is currently pursuing her Ph.D. at the Technological University Dublin, specializing in knowledge graphs. Her current research interests include machine learning and knowledge graphs.

Bojan Božić, SFI Centre for Research Training in Machine Learning, School of Computer Science, Technological University Dublin, Dublin, Republic of Ireland

Bojan Božić is a Lecturer in Computer Science at TU Dublin. He has worked on European research projects such as SANY (Sensor Web Enablement), TaToo (Tagging Tools for Semantic Discovery), Europeana Creative (Cultural Inheritage), PELAGIOS (Linked Data), and C2-SENSE (Sensor Web and Interoperability). He also has contributed to the H2020 project ALIGNED, modelling data and software engineering processes through ontologies and annotations for the Dacura platform. His current research interests are Semantic Web, machine learning, and natural language processing.

Luca Longo, SFI Centre for Research Training in Machine Learning, School of Computer Science, Technological University Dublin, Dublin, Republic of Ireland

Luca Longo is a curious individual deeply devoted to and highly passionate for science. He strives for excellence and contribution to knowledge. He received his doctoral degree in Artificial Intelligence at Trinity College Dublin after a bachelor and masters in Computer Science, Statistics and Health Informatics. He is actively engaged in dissemination of scientific material to the public as his TEDx talks demonstrate. He has received various awards both for his research work and for his teaching. With his team of doctoral and post-doctoral students, he conducts fundamental research in explainable artificial intelligence, defeasible reasoning, and non-monotonic argumentation. He also performs applied research in machine learning and predictive data analytics, mainly applied to the problem of mental workload modelling.


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How to Cite

Nayak, A. ., Božić, B. ., & Longo, L. . (2023). Data Quality Assessment and Recommendation of Feature Selection Algorithms: An Ontological Approach. Journal of Web Engineering, 22(01), 175–196.