Automated Semantic Annotation Deploying Machine Learning Approaches: A Systematic Review

  • Wee Chea Chang Faculty of Technology and Applied Science, Open University Malaysia, Malaysia
  • Anbuselvan Sangodiah School of Computing, Faculty of Computing and Engineering, Quest International University, Malaysia
Keywords: Semantic Web, Semantic Annotation Automation, Machine Learning, Quality Metrics, Systematic Review


Semantic Web is the vision to make Internet data machine-readable to achieve information retrieval with higher granularity and personalisation. Semantic annotation is the process that binds machine-understandable descriptions into Web resources such as text and images. Hence, the success of Semantic Web depends
on the wide availability of semantically annotated Web resources. However, there remains a huge amount of unannotated Web resources due to the limited annotation capability available. In order to address this, machine learning approaches have been used to improve the automation process. This Systematic Review aims to summarise the existing state-of-the-art literature to answer five Research Questions focusing on machine learning driven semantic annotation automation. The analysis of 40 selected primary studies reveals that the use of unitary and combination of machine learning algorithms are both the current directions. Support
Vector Machine (SVM) is the most-used algorithm, and supervised learning is the predominant machine learning type. Both semi-automated and fully automated annotation are almost nearly achieved. Meanwhile, text is the most annotated Web resource; and the availability of third-party annotation tools is in-line with this. While Precision, Recall, F-Measure and Accuracy are the most deployed quality metrics, not all the studies measured the quality of the annotated results. In the future, standardising quality measures is the direction for research.


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How to Cite
Chang, W.C. and Sangodiah, A. 2023. Automated Semantic Annotation Deploying Machine Learning Approaches: A Systematic Review. MENDEL. 29, 2 (Dec. 2023), 111-130. DOI:
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