Connectionist Models for Formal Knowledge Adaptation

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.

• Type: Conference
• Year: 2009
• Where: ICANN
• Authors: Ilianna Kollia, Nikos Simou, Giorgos B. Stamou, Andreas Stafylopatis
• Keywords: Artificial Intelligence, Artificial Neural Networks, ICANN 2009, Image Segment Classification, Symbolic Knowledge Representation