Abstract
The growing interest in the semantic web technologies in the past years has led to the increase in the number of ontologies on the web. This gives semantic web developers the opportunity to select and reuse these ontologies in new applications. However, none of the existing approaches has leveraged the power of Machine Learning to assist in the choice of suitable ontologies for reuse. In this paper, the k-Nearest Neighbors (KNN) algorithm is implemented to classify ontologies based on their quality metrics. The aim is to group the ontologies that display the same quality properties into classes, thereby, providing some insights into the selection and reuse of these ontologies using a Machine Learning technique. The experiments were carried out with a dataset of 200 biomedical ontologies characterized each by 11 quality metric attributes. The KNN model was trained and tested with 70% and 30% of the dataset, respectively. The evaluation of the KNN model was undertaken with various metrics including accuracy, precision, recall, F-measure and Receiver Operating Characteristic (ROC) curves. For the best value of k = 5 the KNN model displayed promising results with an accuracy of 67% and the average precision, recall, and F-measure of 69%, 67%, and 67%, respectively as well as an area under ROC curve of 0.78.
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References
Trokanas, N., Cecelja, F.: Ontology evaluation for reuse in the domain of process systems engineering. Comput. Chem. Eng. 85, 177–187 (2016)
Alani, H., Brewster, C., Shadbolt, N.: Ranking ontologies with AKTiveRank. In: 5th International Conference on the Semantic Web, pp. 1–15. Athens, Greece (2006)
dAquin, M., Noy, N.F.: Where to publish and find ontologies? A survey of ontology libraries. Web Semant. Sci. Serv. Agents World Wide Web 11, 96–111 (2012)
Lantow, B.: OntoMetrics: putting metrics into use for ontology evaluation. In: Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2016), pp. 186–191, Joaquim Filipe, Portugal (2016)
Wolstencroft, K., Lord, P., Tabernero, L., Brass, A., Stevens, R.: Protein classification using ontology classification. Bioinformatics 22, e530–e538 (2006)
Glimm, B., Horrocks, I., Motik, B., Shearer, R., Stoilos, G.: A novel approach to ontology classification. J. Seman. Web 14, 84–101 (2012)
Wang, C., Feng, Z., Zhang, X., Wang, X., Rao, G., Fu, D.: ComR: a combined OWL reasoner for ontology classification. Front. Comput. Sci. 13, 139–156 (2019)
Zhao, Y., Dong, J., Peng, T.: Ontology classification for semantic-web-based software engineering. IEEE Trans. Serv. Comput. 2, 303–317 (2009)
Jurisch, M., Igler, B.: RDF2Vec-based classification of ontology alignment changes. In: 1st Workshop on Deep Learning for Knowledge Graphs and Semantic Technologies (DL4KGS) co-located with the 15th Extended Semantic Web Conerence (ESWC 2018) (2018)
Fan, G.F., Guo, Y.H., Zheng, J.M., Hong, W.C.: Application of the weighted K-nearest neighbor algorithm for short-term load forecasting. Energies 12, 1–19 (2019)
Jabbar, M.A., Deekshatulu, B.L., Chandra, P.: Classification of heart disease using K-nearest neighbor and genetic algorithm. Procedia Technol. 10, 85–94 (2013)
Luschow, A., Wartena, C.: Classifying medical literature using k-nearest-neighbours algorithm. In: NKOS Workshop, pp. 1–13 (2017)
Sharman, R., Rao, H.R., Raghu, T.S. (eds.): WEB 2009. LNBIP, vol. 52. Springer, Heidelberg. https://doi.org/10.1007/978-3-642-17449-0
Harrison, O: Machine learning basics with the K-nearest neighbors algorithm. Towards Data Sci. (2018)
Denoeux, T.: A K-Nearest Neighbor Classification Rule Based on Dempster-Shafer Theory. International Journal on Transactions on Systems, Man and Cybernetics. 25, 804–813 (1995)
Hand, D., Mannila, H., Smyth, P.: Principles of Data mining, pp. 242–267. The MIT Press, Cambridge (2001)
Zhang, M., Zhou, Z.: A k-nearest neighbor based algorithm for multi-label classification. In: IEEE International Conference on Granular Computing, pp. 718–721 (2005)
Potamias, M., Bonchi, F., Gionis, A., Kollios, G.: K-nearest neighbors in uncertain graphs. J. VLDB Endowment 3, 997–1008 (2010)
Clare, A., King, R.D.: Knowledge discovery in multi-label phenotype data. In: De Raedt, L., Siebes, A. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 42–53. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44794-6_4
Myles, J., Hand, D.: The multi-class metric problem in nearest neighbor discrimination rules. Int. J. Patt. Recogn. 23, 1291–1297 (1990)
Noy, N.F., et al.: BioPortal: ontologies and integrated data resources at the click of a mouse. In: International Conference on Biomedical Ontology, p. 197. New York, USA (2009)
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Koech, G., Fonou-Dombeu, J.V. (2021). K-Nearest Neighbors Classification of Semantic Web Ontologies. In: Attiogbé, C., Ben Yahia, S. (eds) Model and Data Engineering. MEDI 2021. Lecture Notes in Computer Science(), vol 12732. Springer, Cham. https://doi.org/10.1007/978-3-030-78428-7_19
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