Attribute Value Ontology - Using Semantics in Data Mining

Tomasz Łukaszewski, Joanna Józefowska, Agnieszka Ławrynowicz

Abstract

We propose a new concept to represent attribute values as an ontology that allows modeling different levels of abstraction. In this way more or less precise values may be used instead of missing or erroneous data. The goal is to use this representation in order to improve analysis of imperfect data. The proposed attribute value ontology (AVO) allows to upgrade the precision of information not only from positive observations but also from negative ones. We show how to classify a new example using a set of training examples described in the same or more precise way. Another advantage of the proposed approach is providing an efficient way to avoid the effect of overfitting.

References

  1. Almuallim, H., Akiba, Y., and Kaneda, S. (1996). An efficient algorithm for finding optimal gain-ratio multiple-split tests on hierarchical attributes in decision tree learning. In Proceedings of the Thirteenth National Conference on Artificial Intelligence. AAAI Press.
  2. Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. (1984). Classification and Regression Trees. Wadsworth, Belmont, California, 3rd edition.
  3. Han, J., Cai, Y., and Cercone, N. (1992). Knowledge discovery in databases: An attribute-oriented approach. In Proceedings of the 18th International Conference on Very Large Data Bases. Morgan Kaufmann.
  4. Haussler, D. (1988). Quantifying inductive bias: Ai learning algorithms and valiant's learning framework. In Artif. Intell., Vol. 36(2). Elsevier.
  5. Kudoh, Y., Haraguchi, M., and Okubo, Y. (2003). Data abstractions for decision tree induction. In Theoretical Computer Science, Vol. 292(1). Elsevier.
  6. Nún˜ez, M. (1991). The use of background knowledge in decision tree induction. In Machine Learning, Vol. 6(3). Springer.
  7. Tanaka, H. (1996). Decision tree learning algorithm with structured attributes: Application to verbal case frame acquisition. In Proceedings of the 16th International Conference on Computational Linguistics. Center for Sprogteknologi, Danmark.
  8. Taylor, M. G., Stoffel, K., and Hendler, J. A. (1997). Ontology-based induction of high level classification rules. In Proceedings of the Workshop on Research Issues on Data Mining and Knowledge Discovery. ACM.
  9. Walker, A. (1980). On retrieval from a small version of a large data base. In Proceedings of the Sixth International Conference on Very Large Data Bases. IEEE Computer Society.
  10. Witten, I., Frank, E., and Hall, M. (2011). Data Mining. Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Burlington, 3rd edition.
  11. Zhang, J., Kang, D., Silvescu, A., and Honavar, V. (2006). Learning accurate and concise naive bayes classifiers from attribute value taxonomies and data. In Knowl. Inf. Syst., Vol. 9(2). Springer.
  12. Zhang, J., Silvescu, A., and Honavar, V. (2002). Ontologydriven induction of decision trees at multiple levels of abstraction. In LNCS Vol. 2371. Springer.
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Paper Citation


in Harvard Style

Łukaszewski T., Józefowska J. and Ławrynowicz A. (2012). Attribute Value Ontology - Using Semantics in Data Mining . In Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 2: SCOE, (ICEIS 2012) ISBN 978-989-8565-11-2, pages 329-334. DOI: 10.5220/0004157003290334


in Bibtex Style

@conference{scoe12,
author={Tomasz Łukaszewski and Joanna Józefowska and Agnieszka Ławrynowicz},
title={Attribute Value Ontology - Using Semantics in Data Mining},
booktitle={Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 2: SCOE, (ICEIS 2012)},
year={2012},
pages={329-334},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004157003290334},
isbn={978-989-8565-11-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 2: SCOE, (ICEIS 2012)
TI - Attribute Value Ontology - Using Semantics in Data Mining
SN - 978-989-8565-11-2
AU - Łukaszewski T.
AU - Józefowska J.
AU - Ławrynowicz A.
PY - 2012
SP - 329
EP - 334
DO - 10.5220/0004157003290334