A SYSTEMIC METHODOLOGY FOR ONTOLOGY LEARNING - An Academic Case Study and Evaluation

Richard Gil, Leonardo Contreras, María J. Martín-Bautista

2010

Abstract

There is an important dispersion of technical and methodological resources to support the complete Ontology Learning (OL) process from diverse knowledge sources. This fact makes the maintaining of the structures of representation (ontologies) difficult. Therefore, the Knowledge-based Systems associated with user’s domains may not fulfil the increasing knowledge requirement from the user. In this paper, we give a possible solution for this problem. For this purpose, we propose a Systemic Methodology for OL (SMOL) that unifies and simplifies to the users the whole process of OL from different knowledge sources (ontologies, texts and databases). SMOL as methodology is evaluated under DESMET methods, in addition with their application for an academic case study is also included.

References

  1. Abdullah, M., Kimble, C., Benest, I., and Paige, R. (2006). Knowledge-based systems: a re-evaluation. Journal of Knowledge Management, 10 Nro 3:127-142.
  2. Astrova, I., Korda, N., and Kalja, A. (2007). Rule-based transformation of sql relational databases to owl ontologies. Proceedings of the 2nd International Conference on Metadata & Semantics Research.
  3. Baskerville, R. (1999). Investigating information systems with action research. Comm.AIS, v:2: Art 19.
  4. Borges, A., Corniell, M., Gil, R., Contreras, L., and Borges, R. (2008). Towards a study opportunities recommender system in ontological principles-based on semantic web environment. In The 4th WSEAS/IASME. (EDUTE'08), ACM vol 8, no 2.
  5. Buitelaar, P. and Cimiano, P. (2008). Ontology Learning and Population: Briging the Gap Between Text And Knowledge. IOS Press, Netherland.
  6. Callaos, N. (1992). A systemic system methodology. In International Conference on System Research Informatic and Cybernetics, Baden-Baden, Germany.
  7. Callaos, N. and Callaos, B. (2003). Toward a practical general system methodological theory. Journal of Systemics, Cybernetics and Informatics, 1:114-120.
  8. Callaos, N. and Callaos, B. (2006). Designing with a system total quality. In on Information System Analysis, and Synthesis, ISAS'06, USA, p 15-23.
  9. Cerbah, F. (2009). RDBToOnto User Guide, Version 1.2 Beta From relational Database to Fine-Tuned Populated Ontologies. http://www.tao-project.eu/
  10. Cimiano, P. (2006). Ontology Learning and Population from Text: Algorithms, Evaluation and Applications. Springer-Verlag New York, LLC.
  11. Dahlem, N. and Hahn, A. (2009). User-friendly ontology creation methodologies-a survey. 15th Amer. Conf. On Information Systems, California-USA, pages 1-9.
  12. De-Nicola, A., Missikoff, M., and Navigli, R. (2009). A software engineering approach to ontology building. Information Systems, Elsevier, 34:258-275.
  13. Decker, S., Melnik, S., Van-Harmelen, F., Klein, D., Fensel, F., Broekstra, M., Erdmann, J., and Horrocks, M. (2000). Knowledge networking the semantic web: The roles of xml and rdf. IEEE Internet Computing.
  14. Dilworth, R. (1998). Action learning in a nutshell. Performance Improvement Quarterly, Vol 11(1).
  15. Ehrig, M. (2007). Ontology Alignment: Biding the Semantic Gap. Book, Springer-Verlag.
  16. Euzenat, J., Mocan, A., and Sharffe, F. (2007). Ontology Management, Ontology Alignments: An Ontology Management Perspective. Book, Springer.
  17. Gacitua, R., Sawyer, P., and Rayson, P. (2008). A flexible framework to experiment with ontology learning techniques. Know.-Based Syst., 21(3):192-199.
  18. Garruzzo, S., Rosaci, D., and Sarné, G. (2007). Mars: An agent-based recommender system for the semantic web. LNCS, 4531:181-194.
  19. Gil, R. (2009). New systemic methodology framework for ontology learning (in spanish). Master's thesis, Dpto.Computer Science, Granada University, Spain.
  20. Gil, R., A.Borges, Ramos, L., and Contreras, L. (2008). Ontologies integration for university institutions: Approach to an alignment evaluation. In Proc. 19th Australian Conference on Software Engineering ASWEC 2008, pages 570-578.
  21. Gil, R., Borges, A., Contreras, L., and Martín-Bautista, M. (2009). Improving ontologies through ontology learning: a university case. In CSIE'09, IEEE Computer Society, March-April, L.A.-USA.
  22. Gil, R., Martín-Bautista, M., and Contreras, L. (2010). Applying an ontology learning methodology to a relational database: University case study. IEEEICSC-2010, USA, Sep 22-24 (Accepted).
  23. Gliozzo, A., C. Caracciolo, M. D-Aquin, M. S., Peter, W., Voelker, J., Dzbor, M., Mota, E., Gomez-Perez, A., Haase, P., Waterfield, W., Contreras, J., Grobelink, M., Euzenat, J., Cunning, H., Staab, S., Gangemi, A., Angele, J., Iglesias, M., Lobo, T., and Lopez, A. (2007). Results from experiments in ontology learning including evaluation and recommendation. Technical Report, http://www.neon-project.org/
  24. Gómez-Pérez, A., Fernando-López, M., and Corcho, O. (2004). Ontology Engineering. Book, Springer-Verlag, London- UK.
  25. Gómez-Pérez, A. and Manzano-Macho, D. (2005). An overview of methods and tools for ontology learning from text. Knowledge Engineer. Rev., 19:187-212.
  26. Haase, P., Volker, J., and Sure, Y. (2005). Management of dynamic knowledge. Journal of Knowledge Management, 9:97-107.
  27. Hill, T. and Westbrook, R. (1997). Swot analysis: It's time for a product recall. Long Range Planning, 30:46-52.
  28. Kitchenham, B. (1996). Evaluating software engineering methods and tool. part 3: Selecting an appropriate evaluation method. ACM SIGSOFT Software Engineering Notes, 21:9-12.
  29. Maedche, A. and Staab, S. (2001). Ontology learning for the semantic web. Intel. Systems, IEEE, 16:2:72-79.
  30. Nonaka, I. (1994). A dynamic theory of organizational knowledge creation. Organization Science, 5(1):14-37.
  31. Novacek, V., Laera, L., and S.Handschuh (2007). Semiautomatic integration of learned ontologies into a collaborative framework. In Proceedings of IWOD/ESWC, and ESWC 2007.
  32. Noy, N. and Klein, M. (2004). Ontology evolution: Not the same as schema evolution. Knowledge and Information Systems, 6:428-440.
  33. Noy, N. and Musen, M. (2000). Prompt: Algorithm and tool for automated ontology merging and alignment. Proceeding of National Conference On Artificial Intelligence.
  34. Nyulas, C., O'Connor, M., and Tu, S. (2007). Datamaster - a plug-in for importing schemas and data from relational databases into protege. 10th Intl. Protege Conference, Budapest (2007).
  35. Pressman, R. (2006). Software Engineering: A Practioners Approach. Sixth Edition McGraw-Hill, New York 2006.
  36. Ramos, L. and Gil, R. (2007). Propuesta de sistema de información para apoyar la gestión de la educación a distancia. CISCI'07, July. Orlando-USA.
  37. Shamsfard, N. and Abdollahzadeh, A. (2003). The state of the art in ontology learning: a framework for comparison. Know. Enginer. Rev, 18 (4):293-316.
  38. Simperl, E., Tempich, C., and Vrandecic, D. (2008). A Methodology for Ontology Learning. Chapter of Book Ontology Learning & Population. IOS Press, Buitelaar & Cimiano Eds.
  39. Sommerville, I. (2006). Software Engineering. Pearson Education.
  40. Yao, Y., Zeng, Y., Zhong, N., and Huang, X. (2007). Knowledge retrieval. In Proceedings IEEE/WIC/ACM International Conference on Web Intelligence.
  41. Zhou, L. (2007). Ontology learning: state of the art and open issues. Information Technology and Management, 8(3):241-252.
Download


Paper Citation


in Harvard Style

Gil R., Contreras L. and J. Martín-Bautista M. (2010). A SYSTEMIC METHODOLOGY FOR ONTOLOGY LEARNING - An Academic Case Study and Evaluation . In Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2010) ISBN 978-989-8425-29-4, pages 206-212. DOI: 10.5220/0003070602060212


in Bibtex Style

@conference{keod10,
author={Richard Gil and Leonardo Contreras and María J. Martín-Bautista},
title={A SYSTEMIC METHODOLOGY FOR ONTOLOGY LEARNING - An Academic Case Study and Evaluation},
booktitle={Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2010)},
year={2010},
pages={206-212},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003070602060212},
isbn={978-989-8425-29-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2010)
TI - A SYSTEMIC METHODOLOGY FOR ONTOLOGY LEARNING - An Academic Case Study and Evaluation
SN - 978-989-8425-29-4
AU - Gil R.
AU - Contreras L.
AU - J. Martín-Bautista M.
PY - 2010
SP - 206
EP - 212
DO - 10.5220/0003070602060212