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Tensor-Based Syntactic Feature Engineering for Ontology Instance Matching

  • Andrzej Szwabe
  • Paweł Misiorek
  • Jarosław Bąk
  • Michał Ciesielczyk
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10246)

Abstract

We investigate a machine learning approach to ontology instance matching. We apply syntactic and lexical text analysis as well as tensor-based data representation as means for feature engineering effectively supporting supervised learning based on logistic regression. We experimentally evaluate our approach in the scenario of the SABINE Data linking subtask defined by Ontology Alignment Evaluation Initiative. We show that, as far as the prediction of non-trivial matches is concerned, the use of the proposed tensor-based modelling of lexical and syntactical properties of the ontology instances enables achieving a significant quality improvement.

Keywords

OAEI Ontology instance matching Machine learning Tensor-based data modeling Natural Language Processing Syntactic analysis 

Notes

Acknowledgments

This work is supported by the Polish National Science Centre, grant DEC-2011/01/D/ST6/06788.

References

  1. 1.
    Achichi, M., et al.: Results of the ontology alignment evaluation initiative 2016. In: Proceedings of the 11th International Workshop on Ontology Matching, Kobe, Japan, 18 October 2016, pp. 73–129 (2016). http://ceur-ws.org/Vol-1766/oaei16_paper0.pdf
  2. 2.
    Balasubramani, B.S., Taheri, A., Cruz, I.F.: User involvement in ontology matching using an online active learning approach. In: Proceedings of the 10th International Workshop on Ontology Matching, Bethlehem, PA, USA, 12 October 2015, pp. 45–49 (2015). http://ceur-ws.org/Vol-1545/om2015_TSpaper3.pdf
  3. 3.
    Chapelle, O., Manavoglu, E., Rosales, R.: Simple and scalable response prediction for display advertising. ACM Trans. Intell. Syst. Technol. 5(4), 61:1–61:34 (2014)CrossRefGoogle Scholar
  4. 4.
    Duan, S., Fokoue, A., Srinivas, K.: One size does not fit all: customizing ontology alignment using user feedback. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010. LNCS, vol. 6496, pp. 177–192. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-17746-0_12 CrossRefGoogle Scholar
  5. 5.
    Faria, D., Pesquita, C., Balasubramani, B.S., Martins, C., Cardoso, J., Curado, H., Couto, F., Cruz, I.: OAEI 2016 results of AML. In: Proceedings of the 11th International Workshop on Ontology Matching, Kobe, Japan, 18 October 2016, pp. 138–145 (2016). http://ceur-ws.org/Vol-1766/oaei16_paper2.pdf
  6. 6.
    Gondek, D.C., Lally, A., Kalyanpur, A., Murdock, J.W., Duboue, P.A., Zhang, L., Pan, Y., Qiu, Z.M., Welty, C.: A framework for merging and ranking of answers in DeepQA. IBM J. Res. Dev. 56(3), 399–410 (2012)Google Scholar
  7. 7.
    Johnson, E., Baughman, W.C., Ozsoyoglu, G.: Mixing domain rules with machine learning for radiology text classification. In: Proceedings of the ACM SIGKDD Workshop on Health Informatics (HI-KDD 2014) (2014)Google Scholar
  8. 8.
    Langford, J., Li, L., Zhang, T.: Sparse online learning via truncated gradient. J. Mach. Learn. Res. 10, 777–801 (2009)MathSciNetzbMATHGoogle Scholar
  9. 9.
    Mao, M., Peng, Y., Spring, M.: Ontology mapping: as a binary classification problem. Concurr. Comput. Pract. Exp. 23(9), 1010–1025 (2011)CrossRefGoogle Scholar
  10. 10.
    Nickel, M., Tresp, V.: An analysis of tensor models for learning on structured data. In: Blockeel, H., Kersting, K., Nijssen, S., Železný, F. (eds.) ECML PKDD 2013. LNCS, vol. 8189, pp. 272–287. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-40991-2_18 CrossRefGoogle Scholar
  11. 11.
    Ontology Alignment Evaluation Initiative: OAEI 2016: Instance Matching Track (2016). http://islab.di.unimi.it/content/im_oaei/2016
  12. 12.
    Ontology Alignment Evaluation Initiative: Ontology Alignment Evaluation Initiative - OAEI 2016 Campaign (2016). http://oaei.ontologymatching.org/2016/
  13. 13.
    Otero-Cerdeira, L., Rodríguez-Martínez, F.J., Gómez-Rodríguez, A.: Ontology matching: a literature review. Expert Syst. Appl. 42(2), 949–971 (2015)CrossRefGoogle Scholar
  14. 14.
    Shvaiko, P., Euzenat, J.: Ontology matching: state of the art and future challenges. IEEE Trans. Knowl. Data Eng. 25(1), 158–176 (2013)CrossRefGoogle Scholar
  15. 15.
    Szwabe, A., Misiorek, P., Ciesielczyk, M.: Tensor-based modeling of temporal features for big data CTR estimation. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds.) BDAS 2017. CCIS, vol. 716, pp. 16–27. Springer International Publishing, Cham (2017). doi: 10.1007/978-3-319-58274-0_2 Google Scholar
  16. 16.
    Szwabe, A., Misiorek, P., Walkowiak, P.: Reflective relational learning for ontology alignment. In: Omatu, S., De Paz Santana, J., González, S., Molina, J., Bernardos, A., Rodríguez, J. (eds.) Distributed Computing and Artificial Intelligence. AISC, vol. 151, pp. 519–526. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-28765-7_62
  17. 17.
    Szwabe, A., Misiorek, P., Walkowiak, P.: Tensor-based relational learning for ontology matching. In: Advances in Knowledge-Based and Intelligent Information and Engineering Systems - 16th Annual KES Conference, San Sebastian, Spain, 10–12 September 2012, pp. 509–518 (2012)Google Scholar
  18. 18.
    The Stanford Natural Language Processing Group: The Stanford Parser. http://nlp.stanford.edu/software/lex-parser.shtml
  19. 19.
    Zhang, L., Yang, J., Chu, W., Tseng, B.: A machine-learned proactive moderation system for auction fraud detection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, CIKM 2011, pp. 2501–2504. ACM, New York (2011)Google Scholar
  20. 20.
    Zhang, Y., Jin, H., Pan, L., Li, J.: RiMOM results for OAEI 2016. In: Proceedings of the 11th International Workshop on Ontology Matching, Kobe, Japan, 18 October 2016, pp. 210–216 (2016). http://ceur-ws.org/Vol-1766/oaei16_paper13.pdf

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Andrzej Szwabe
    • 1
  • Paweł Misiorek
    • 1
  • Jarosław Bąk
    • 1
  • Michał Ciesielczyk
    • 1
  1. 1.Institute of Control and Information EngineeringPoznan University of TechnologyPoznanPoland

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