OntoDM: An ontology of data mining

Proceedings - IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008

Volumn , Issue , 2008, Pages 752-760

  Panov, Panče ^a   Džeroski, Sašo ^a   Soldatova, Larisa N ^b  

^a JO EF STEFAN INSTITUTE   (Slovenia)

^b ABERYSTWYTH UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; INFORMATION MANAGEMENT; TECHNICAL PRESENTATIONS;

BEST PRACTICES; COMPLEX ENTITIES; CONSTRAINT-BASED DATA MININGS; DATA MINING ALGORITHMS; DATA MINING TASKS; DATA SETS; DATA TYPES; DISTANCE FUNCTIONS; GROWING DEMANDS; MULTIPLE INHERITANCES; ONTOLOGY ENGINEERINGS;

ONTOLOGY;

EID: 62449284675     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDMW.2008.62     Document Type: Conference Paper

References (30)

1. M. Ashburner, C. A. Ball, J. A. Blake, D. Botstein, H. Butler, J. M. Cherry, A. P. Davis, K. Dolinski, S. S. Dwight, J. T. Eppig, M. A. Harris, D. P. Hill, L. Issel-Tarver, A. Kasarskis, S. Lewis, J. C. Matese, J. E. Richardson, M. Ringwald, G. M. Rubin, and G. Sherlock. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet, 25(1):25-29, May 2000.
2. C. A. Ball and A. Brazma. MGED standards: work in progress. Omics : a journal of integrative biology, 10(2):138-144, 2006.
3. A. Bernstein, F. Provost, and S. Hill. Toward intelligent assistance for a data mining process: An ontology-based approach for cost-sensitive classification. IEEE Trans. on Knowl. and Data Eng., 17(4):503-518, 2005.
4. J.-F. Boulicaut, M. Klemettinen, and H. Mannila. Modeling KDD processes within the inductive database framework. In Data Warehousing and Knowledge Discovery, pages 293-302, 1999.
5. A. Brazma et al. Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nature Genetics, 29:365-371, December 2001.
6. P. Brezany, I. Janciak, and A. M. Tjoa. Data Mining with Ontologies: Implementations, Findings and Frameworks, chapter Ontology-Based Construction of Grid Data Mining Workflows. IGI Global, 2007.
7. M. Cannataro and C. Comito. A data mining ontology for grid programming. In Proceedings of the 1st Int. Workshop on Semantics in Peer-to-Peer and Grid Computing (SemP-Grid2003), pages 113-134, 2003.
8. M. Cannataro and D. Talia. The knowledge grid. Commun. ACM, 46(1):89-93, 2003.
9. B. Chandrasekaran, J. R. Josephson, and V. R. Benjamins. What are ontologies, and why do we need them? IEEE Intelligent Systems, 14(1):20-26, 1999.
10. P. Chapman, R. Kerber, J. Clinton, T. Khabaza, T. Reinartz, and R. Wirth. The CRISP-DM process model. Discussion Paper, March 1999. http://www.crisp-dm. org.
11. S. Džeroski. Towards a general framework for data mining. In S. Dzeroski and J. Struyf, editors, KDID, volume 4747 of Lecture Notes in Computer Science, pages 259-300. Springer, 2006.
12. 13 pc NMR spectra. In S. Muggleton, editor, Inductive Logic Programming Workshop, volume 1314 of Lecture Notes in Computer Science, pages 41-54. Springer, 1996.
13. A. Gangemi, N. Guarino, C. Masolo, A. Oltramari, and L. Schneider. Sweetening ontologies with DOLCE, 2002.
14. M. F. Hornick, E. Marcadé, and S. Venkayala. Java Data Mining: Strategy, Standard, and Practice: A Practical Guide for architecture, design, and implementation (The Morgan Kaufmann Series in Data Management Systems). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2006.
15. T. Imielinski and H. Mannila. A database perspective on knowledge discovery. Comm. Of The Acm, 39:58-64, 1996.
16. A. Kalousis, A. Bernstein, and M. Hilario. Meta-learning with kernels and similarity functions for planning of data mining workflows. In P. Brazdil, A. Bernstein, and L. Hunter, editors, Proceedings of the Second Planning to Learn Workshop (PlanLearn) at the ICML/COLT/UAI 2008, pages 23-28, 2008.
17. R. Mizoguchi. Tutorial on ontological engineering - part 3: Advanced course of ontological engineering. New Generation Comput., 22(2), 2004.
18. R. Ramakrishnan, R. Agrawal, J.-C. Freytag, T. Bollinger, C. W. Clifton, S. Dzeroski, J. Hipp, D. Keim, S. Kramer, H.-P. Kriegel, U. Leser, B. Liu, H. Mannila, R. Meo, S. Morishita, R. Ng, J. Pei, P. Raghavan, M. Spiliopoulou, J. Srivastava, and V. Torra. Data mining: The next generation. In R. Agrawal, J. C. Freytag, and R. Ramakrishnan, editors, Perspectives Workshop: Data Mining: The Next Generation, number 04292 in Dagstuhl Seminar Proceedings, Dagstuhl, Germany, 2005. Internationales Begegnungs-und Forschungszentrum für Informatik (IBFI), Schloss Dagstuhl, Germany.
19. C. Rosse and J. L. V. Mejino. A reference ontology for biomedical informatics: the foundational model of anatomy. J. of Biomedical Informatics, 36(6):478-500, December 2003.
20. S.-A. Sansone at al. Metabolomics standards initiative - ontology working group. work in progress. Metabolomics, 3(3):249-256, 2007.
21. D. Schober, W. Kusnierczyk, S. E. Lewis, and J. Lomax. Towards naming conventions for use in controlled vocabulary and ontology engineering. In Proceedings of BioOntologies SIG, ISMB 2007, pages 29-32, 2007.
22. B. Smith, W. Ceusters, B. Klagges, J. Kohler, A. Kumar, J. Lomax, C. Mungall, F. Neuhaus, A. L. Rector, and C. Rosse. Relations in biomedical ontologies. Genome Biology, 6(5), 2005.
23. B. Smith and N. Shah. Ontologies for biomedicine - how to make them and use them. Tutorial notes at ISMB/ECCB 2007, 2007.
24. L. N. Soldatova, W. Aubrey, R. D. King, and A. Clare. The exact description of biomedical protocols. Bioinformatics, 24(13), 2008.
25. L. N. Soldatova and R. D. King. An ontology of scientific experiments. Journal of the Royal Society Interface, 3(11):795-803, 2006.
26. R. D. Stevens, P. G. Baker, S. Bechhofer, G. Ng, A. Jacoby, N. Paton, C. A. Goble, and A. Brass. Tambis: Transparent access to multiple bioinformatics information sources. Bioinformatics, 16:200-0, 2000.
27. C. F. Taylor at al. The minimum information about a proteomics experiment (miape). Nature Biotechnology, (25):887-893, 2007.
28. I. H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann Series in Data Management Systems. Morgan Kaufmann, second edition, June 2005.
29. Q. Yang and X. Wu. 10 challenging problems in data mining research. International Journal of Information Technology and Decision Making, 5(4):597-604, 2006.
30. M. Žáková, P. Kremen, F. Železný, and N. Lavrač. Planning to learn with a knowledge discovery ontology. In P. Brazdil, A. Bernstein, and L. Hunter, editors, Proceedings of the Second Planning to Learn Workshop (PlanLearn) at the ICML/COLT/UAI2008, pages 29-34, 2008.