Connectionist Models for Formal Knowledge Adaptation

Authors:
Ilianna Kollia;Nikolaos Simou;Giorgos Stamou;Andreas Stafylopatis
Affiliations:
Department of Electrical and Computer Engineering, National Technical University of Athens, Zographou, Greece 15780;Department of Electrical and Computer Engineering, National Technical University of Athens, Zographou, Greece 15780;Department of Electrical and Computer Engineering, National Technical University of Athens, Zographou, Greece 15780;Department of Electrical and Computer Engineering, National Technical University of Athens, Zographou, Greece 15780
Venue:
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Year:
2009

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Abstract

Both symbolic knowledge representation systems and artificial neural networks play a significant role in Artificial Intelligence. A recent trend in the field aims at interweaving these techniques, in order to improve robustness and performance of classification and clustering systems. In this paper, we present a novel architecture based on the connectionist adaptation of ontological knowledge. The proposed architecture was used effectively to improve image segment classification within a multimedia application scenario.