Connectionist Models for Formal Knowledge Adaptation

Ilianna Kollia, Nikos Simou, Giorgos B. Stamou, Andreas Stafylopatis

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.