In recent years, substantial progress has been made towards the use of neural networks for structured knowledge. Structured domains are characterized by complex patterns usually represented as lists, trees, and graphs of variable sizes and complexity. The basic problem is to overcome the limitation imposed by the fixed input size of a network when faced with the task of representing a graph of variable size and with an internal structure. Standard neural networks are well suited for dealing with unstructured patterns, and recurrent neural networks can be used to process sequences; a generalization of a recurrent neuron, the generalized recursive neuron, is capable of representing, classifying, and storing structured information very naturally.
This tutorial will present various approaches for the use of neural
networks to deal with structured knowledge.
Attendees should be familiar with basic concepts of neural networks, or
have background knowledge in a related area such as machine learning or
About the Lecturers
is co-director of the Software Engineering Lab and associate director of
the Electronic Enterprise Engineering program at the Computer and
Information Systems Department, New Jersey Institute of Technology. His
main area of research is the integration of systems based on various
computation methods, e.g. symbolic and connectionist systems, and in
particular logic and reasoning with neural networks.
Alessandro Sperduti is an Assistant Professor at the Computer Science Department, University of Pisa, Italy. His research area is neural networks, especially for representing structured information. He has written around 40 refereed papers mainly in the areas of neural networks, fuzzy systems, and image processing.