Abstract
Despite their widespread use, machine learning (ML) methods often exhibit sub-optimal performance. The accuracy of these models is primarily hindered by insufficient training data and poor data quality, with particularly severe consequences in critical areas such as medical diagnosis prediction. Our hypothesis is that enhancing ML pipelines with semantic information such as those available in knowledge graphs (KG) can address these challenges and improve ML prediction accuracy. To that end, we extend the state of the art through a novel approach that uses KG embeddings to augment tabular data in various innovative ways within ML pipelines. Concretely, we introduce and examine several integration techniques of KG embeddings and the influence of KG characteristics on model performance, specifically accuracy and F2 scores. We evaluate our approach with four ML algorithms and two embedding techniques, applied to heart and chronic kidney disease prediction. Our results indicate consistent improvements in model performance across various ML models and tasks, thus confirming our hypothesis, e.g. we increased the F2 score for the KNN from 70% to 82.22%, and the F2 score for SVM from 74.53% to 81.71%, for heart disease prediction.
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Notes
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The importance of preprocessing step for achieveing good performance is shown by Hassler et al. [14].
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Acknowledgements
This work was supported by the FWF HOnEst project (V 745-N), FFG SENSE project (894802) and FAIR-AI project (904624).
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A Appendix: Additional Experimental Analysis
A Appendix: Additional Experimental Analysis
Tables in the appendix depicts the information about the experiment setup.
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Table 3 shows the details of the ontologies used for heart and kidney disease domain where classes (or concepts) represent distinct groups e.g., ‘Patient’ or ‘Disease’; object properties describe the relationships between two classes e.g., ‘hasSymptom’ connecting ‘Patient’ to ‘Symptom’; and data properties define characteristics or attributes of classes e.g., ‘Patient’s’ age.
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Table 4 shows the embedding algorithms parameters used. The embeddings are generated with vector sizes of 64, 100, and 128, to ensure a better capture of the semantic knowledge from the KG. The reported results represent the average performance across these three vector dimensions.
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Table 5 shows the parameters used for the ML models.
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Llugiqi, M., Ekaputra, F.J., Sabou, M. (2024). Enhancing Machine Learning Predictions Through Knowledge Graph Embeddings. In: Besold, T.R., d’Avila Garcez, A., Jimenez-Ruiz, E., Confalonieri, R., Madhyastha, P., Wagner, B. (eds) Neural-Symbolic Learning and Reasoning. NeSy 2024. Lecture Notes in Computer Science(), vol 14979. Springer, Cham. https://doi.org/10.1007/978-3-031-71167-1_15
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