Improving the Data Quality of Drug Databases using Conditional Dependencies and Ontologies

Abstract

Many health care systems and services exploit drug related information stored in databases. The poor data quality of these databases, e.g. inaccuracy of drug contraindications, can lead to catastrophic consequences for the health condition of patients. Hence it is important to ensure their quality in terms of data completeness and soundness.

In the database domain, standard Functional Dependencies (FDs) and INclusion Dependencies (INDs), have been proposed to prevent the insertion of incorrect data. But they are generally not expressive enough to represent a domain-specific set of constraints. To this end, conditional dependencies, i.e. standard dependencies extended with tableau patterns containing constant values, have been introduced and several methods have been proposed for their discovery and representation. The quality of drug databases can be considerably improved by their usage.

Moreover, pharmacology information is inherently hierarchical and many standards propose graph structures to represent them, e.g. the Anatomical Therapeutic Chemical classification (ATC) or OpenGalen’s terminology. In this article, we emphasize that the technologies of the Semantic Web are adapted to represent these hierarchical structures, i.e. in RDFS and OWL. We also present a solution for representing conditional dependencies using a query language defined for these graph oriented structures, namely SPARQL. The benefits of this approach are interoperability with applications and ontologies of the Semantic Web as well as a reasoning-based query execution solution to clean underlying databases.

References

1. Abiteboul, S., Hull, R., and Vianu, V. 1995. Foundations of Databases. Addison-Wesley.
2. Baader, F., Calvanese, D., McGuinness, D. L., Nardi, D., and Patel-Schneider, P. F., Eds. 2003. The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press.
3. Batini, C. and Scannapieco, M. 2006. Data Quality: Concepts, Methodologies and Techniques (Data-Centric Systems and Applications). Springer-Verlag, Berlin.
4. Bohannon, P., Fan, W., Geerts, F., Jia, X., and Kementsietsidis, A. 2007. Conditional functional dependencies for data cleaning. In Proceedings of ICDE. IEEE, 746--755.
5. Bravo, L., Fan, W., and Ma, S. 2007. Extending dependencies with conditions. In Proceedings of VLDB. 243--254.
6. Bravo, L., Fan, W., Geerts, F., and Ma, S. 2008. Increasing the expressivity of conditional functional dependencies without extra complexity. In Proceedings of ICDE. IEEE, 516--525.
7. Chiang, F. and Miller, R. J. 2008. Discovering data quality rules. Proc. VLDB 1, 1, 1166--1177.
8. Curé, O. 2004. Ximsa : Extended interactive multimedia system for auto-medication. In Proceedings of CBMS. 570--575.
9. Curé, O. and Bensaid, J.-D. 2008. Integration of relational databases into owl knowledge bases: Demonstration of the DBOM system. In Proceedings of ICDE Workshops. 230--233.
10. Curé, O. and Squelbut, R. 2005. A database trigger strategy to maintain knowledge bases developed via data migration. In Proceedings of EPIA. 206--217.
11. Fan, W. 2008. Dependencies revisited for improving data quality. In Proceedings of PODS. ACM, 159--170.
12. Fan, W., Geerts, F., Laksmanan, L. V., and Xiong, M. 2009. Discovering conditional functional dependencies. In Proceedings of ICDE. 1231--1234.
13. Giroud, J.-P. and Hagege, C. 2001. Le Guide de Tous les Médicaments. Editions du Rocher Paris, France.
14. Goethals, B., Page, W. L., and Mannila, H. 2008. Mining association rules of simple conjunctive queries. In Proceedings of SDM. SIAM, 96--107.
15. Golab, L., Karloff, H. J., Korn, F., Srivastava, D., and Yu, B. 2008. On generating near-optimal tableaux for conditional functional dependencies. Proc. VLDB 1, 1, 376--390.
16. Levy, A. Y., Mendelzon, A. O., Sagiv, Y., and Srivastava, D. 1995. Answering queries using views. In Proceedings of PODS. ACM, 95--104.
17. Marchi, F. D. and Petit, J.-M. 2003. Zigzag: A new algorithm for mining large inclusion dependencies in database. In Proceedings of ICDM. IEEE Computer Society, 27--34.
18. Mitchell, J. C. 1983. The implication problem for functional and inclusion dependencies. Inform. Contr. 56, 3, 154--173.

Index Terms

• Information systems

  • Information systems applications

    • Data mining