Neural Networks for Structured Knowledge
Franz Kurfess and Alessandro Sperduti
Course Description
This tutorial presents an overview of recent developments in using
neural networks for representation and processing of structured
knowledge. To date, mostly symbol-oriented methods have been used for
this purpose; these methods, however, have problems with inexact and
noisy data, inconsistent knowledge, brittleness, knowledge acquisition,
and real-time constraints. Neural networks, on the other hand, are
universal approximators, can perform automatic inference (learning),
possess very good classification capabilities, and can deal with noise
and incomplete data; they can frequently also be used as
anytime-methods, where a (possibly non-optimal) result is available
anytime during the evaluation, not only at the end.
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
statistics.
About the Lecturers
Franz Kurfess
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.
higuchi@etl.go.jp
Last modified: Thu Feb 20 13:36:33 JST 1997