Abstract
The evolution of the Semantic Web depends on the growing number of ontologies it comprises. However, all ontologies have differences in structure and content because there is no unified standard for their design. To ensure interoperability and fluent information exchange, the correspondences between entities of different ontologies must be found and mapped. A lot of methods have already been proposed for matching heterogeneous ontologies, but they still have many shortcomings and require improvements. This paper suggests a novel semi-supervised machine learning method, which solves ontology mapping task as a classification problem with training set, comprised only of labeled positive examples. Negative examples are generated artificially using an entropy measure in order to build a more accurate Naive Bayesian classifier.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Holzschlag, M.E.: 250 HTML and Web Design Secrets. Wiley, Indianapolis (2004)
Antoniou, G., van Harmelen, F.: A Semantic Web Primer. The MIT Press, Cambridge (2008)
Passin, T.B.: Explorer’s Guide to the Semantic Web. Manning, Greenwich (2004)
Baader, F., Horrocks, I., Sattler, U.: Description logics as ontology languages for the semantic web. In: Hutter, D., Stephan, W. (eds.) Mechanizing Mathematical Reasoning. LNCS (LNAI), vol. 2605, pp. 228–248. Springer, Heidelberg (2005)
Antoniou, G., van Harmelen, F.: OWL: web ontology language. In: Handbook on Ontologies, pp. 91–110 (2009)
Euzenat, J., Shvaiko, P.: Ontology Matching. Springer, Heidelberg (2007)
Otero-Cerdeira, L., Rodriguez-Martinez, F.J., Gomez-Rodriguez, A.: Ontology matching: a literature review. Expert Syst. Appl. 42, 949–971 (2015)
Euzenat, J., Shvaiko, P.: Ontology matching: state of the art and future challenges. IEEE Trans. Knowl. Data Eng. 25, 158–176 (2013)
Ontology Alignment Evaluation Initiative. http://oaei.ontologymatching.org/
Liu, L., Yang, F., Zhang, P., Wu, J.Y., Hu, L.: SVM-based ontology mapping approach. Int. J. Autom. Comput. 9, 306–314 (2012)
Kubat, M.: An Introduction to Machine Learning. Springer, Heidelberg (2015)
Li, X.L., Liu, B., Ng, S.K.: Learning to identify unexpected instances in the test set. In: IJCAI 2007 Proceedings of the 20th International Joint Conference on Artifical Intelligence, pp. 2802–2807. Morgan Kaufmann, San Francisco (2007)
Choi, N., Song, I.Y., Han, H.: A survey on ontology mapping. ACM SIGMOD Rec. 35, 34–41 (2006)
Kalfoglou, Y.: Ontology mapping: the state of the art. Knowl. Eng. Rev. 18, 1–31 (2003)
Thanh Le, B., Dieng-Kuntz, R., Gandon, F.: An ontology matching problems for building a corporate Semantic Web in a multi-communities organization. In: 6th International Conference on Enterprise Information Systems (ICEIS), pp. 236–243. Springer, Heidelberg (2004)
Doan, A., Madhavan, J., Domingos, P., Halevy, A.Y: Ontology matching: a machine learning approach. In: Handbook on Ontologies, pp. 385–404 (2004)
Nezdali, A.H., Shadgar, B., Osareh, A.: Ontology alignment using machine learning techniques. Int. J. Comput. Sci. Inf. Technol. 3, 139–150 (2011)
Mao, M., Peng, Y., Spring, M.: Ontology mapping: as a binary classification problem. In: 4th International Conference on Semantics, Knowledge and Grid 2008 (SKG 2008), pp. 20–25. IEEE, New York (2008)
Euzenat, J., Valtchev, P.: Similarity-based ontology alignment for OWL-Lite. In: 16th European Conference on Artificial Intelligence (ECAI), pp. 333–337. IOS Press, Amsterdam (2004)
Castano, S., Ferrara, A., Montanelli, S.: Matching ontologies in open networked systems: techniques and applications. In: Spaccapietra, S., Atzeni, P., Chu, W.W., Catarci, T., Sycara, K. (eds.) Journal on Data Semantics V. LNCS, vol. 3870, pp. 25–63. Springer, Heidelberg (2006)
Daneshpazhouh, A., Sami, A.: Entropy-based outlier detection using semi-supervised approach with few positive examples. Pattern Recogn. Lett. 49, 77–84 (2014)
Zhang, B., Zuo, W.: Reliable negative extracting based on kNN for learning from positive and unlabelled examples. J. Comput. 4, 94–101 (2009)
Li, X., Liu, B.: Learning to classify texts using positive and unlabeled data. In: 18th International Joint Conference on Artificial Intelligence 2003 (IJCAI 2003), pp. 587–592. Morgan Kaufman, San Francisco (2003)
Wang, X., Xu, Z., Sha, C., Ester, M., Zhou, A.: Semi-supervised learning from only positive and unlabeled data using entropy. In: Chen, L., Tang, C., Yang, J., Gao, Y. (eds.) WAIM 2010. LNCS, vol. 6184, pp. 668–679. Springer, Heidelberg (2010)
Li, X.-L., Liu, B., Ng, S.-K.: Learning to classify documents with only a small positive training set. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 201–213. Springer, Heidelberg (2007)
Wang, Y., Liu, W., Bell, D.A.: A structure-based similarity spreading approach for ontology matching. In: Deshpande, A., Hunter, A. (eds.) SUM 2010. LNCS, vol. 6379, pp. 361–374. Springer, Heidelberg (2010)
Doan, A., Madhavan, J., Dhamankar, R., Domingos, P., Halevy, A.: Learning to match ontologies on the Semantic Web. Int. J. Very Large Data Bases (VLDB J.) 12, 303–319 (2003)
Wang, Z.: A semi-supervised learning approach for ontology matching. In: Zhao, D., Du, J., Wang, H., Wang, P., Ji, D., Pan, J.Z. (eds.) CSWS 2014. CCIS, vol. 480, pp. 17–28. Springer, Heidelberg (2014)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Linaburgyte, R., Butleris, R. (2016). Semi-supervised Learning Approach for Ontology Mapping Problem. In: Dregvaite, G., Damasevicius, R. (eds) Information and Software Technologies. ICIST 2016. Communications in Computer and Information Science, vol 639. Springer, Cham. https://doi.org/10.1007/978-3-319-46254-7_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-46254-7_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46253-0
Online ISBN: 978-3-319-46254-7
eBook Packages: Computer ScienceComputer Science (R0)