# A SEMI-AUTOMATIC BAYESIAN ALGORITHM FOR ONTOLOGY LEARNING

### Francesco Colace, Massimo De Santo, Mario Vento, Pasquale Foggia

#### Abstract

The dynamism of the new society forces the professional man to be abreast of technical progress. It is essential to introduce new didactic methodologies based on continuous long-life learning. A good solution can be E-learning. Although distance education environments are able to provide trainees and instructors with cooperative learning atmosphere, where students can share their experiences and teachers guide them in their learning, some problems must be still solved. One of the most important problem to solve is the correct definition of the domain of knowledge (i.e. ontology) related to the various courses. Often teachers are not able to easily formalize in correct way the reference ontology. On the other hand if we want realize some intelligent tutoring system that can help students and teachers during the learning process starting point is the ontology. In addition, the choice of best contents and information for students is closely connect to the ontology. In this paper, we propose a method for learning ontologies used to model a domain in the field of intelligent e-learning systems. This method is based on the use of the formalism of Bayesian networks for representing ontologies, as well as on the use of a learning algorithm that obtains the corresponding probabilistic model starting from the results of the evaluation tests associated with the didactic contents under examination. Finally, we will present an experimental evaluation of the method using data coming from real courses.

#### References

- Gruber T. R., 1993. A translation Approach to Portable Ontology Specifications. In Knowledge Acquisition, 5(2): 199-220
- Neches R., Fikes R. E., Finin T., Gruber T. R., Senator T., Swartout W. R., 1991. Enabling Technology for Knowledge Sharing. In AI Magazine, 12(3):36-56
- Chandrasekaran B., Josephson J. R., Benjamins R., 1999. What are ontologies, and why do we need them?. In IEEE Intelligent Systems, Volume: 14
- Uschold M., Gruninger M., 1992. Ontologies: Principles, Methods and Applications. In Knowledge Engineering Review, volume 11, number 2
- Heckerman, Geiger, Chickering, 2000. Learning Bayesian Networks: The combination of knowledge and statistical data. In Machine Learning, vol.4
- Conati, Gertner, VanLehn, Drudzel, 1997. On-Line Student Modeling for Coached Problem Solving Using Bayesian Networks. In User Modeling: proceedings of the sixth international conference, UM97
- Jensen, F., 1998. An Introduction to Bayesian Networks. Springer - Verlag, New York.
- Heckerman, 1997. Bayesian Networks for Data Mining. In Data Mining and Knowledge Discovery 1
- Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete, 2000. Building Bayesian Network-Based

#### Paper Citation

#### in Harvard Style

Colace F., De Santo M., Vento M. and Foggia P. (2004). **A SEMI-AUTOMATIC BAYESIAN ALGORITHM FOR ONTOLOGY LEARNING** . In *Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS,* ISBN 972-8865-00-7, pages 191-196. DOI: 10.5220/0002625201910196

#### in Bibtex Style

@conference{iceis04,

author={Francesco Colace and Massimo De Santo and Mario Vento and Pasquale Foggia},

title={A SEMI-AUTOMATIC BAYESIAN ALGORITHM FOR ONTOLOGY LEARNING},

booktitle={Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS,},

year={2004},

pages={191-196},

publisher={SciTePress},

organization={INSTICC},

doi={10.5220/0002625201910196},

isbn={972-8865-00-7},

}

#### in EndNote Style

TY - CONF

JO - Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 2: ICEIS,

TI - A SEMI-AUTOMATIC BAYESIAN ALGORITHM FOR ONTOLOGY LEARNING

SN - 972-8865-00-7

AU - Colace F.

AU - De Santo M.

AU - Vento M.

AU - Foggia P.

PY - 2004

SP - 191

EP - 196

DO - 10.5220/0002625201910196