a subfield of Machine Learning which uses Logic Programming as a uniform representation for examples, background knowledge and hypotheses.
Published in Chapter:
FOL Learning for Knowledge Discovery in Documents
Stefano Ferilli (Università degli Studi di Bari, Italy), Floriana Esposito (Università degli Studi di Bari, Italy), Marenglen Biba (Università degli Studi di Bari, Italy), Teresa M.A. Basile (Università degli Studi di Bari, Italy), and Nicola Di Mauro (Università degli Studi di Bari, Italy)
Copyright: © 2010
|Pages: 27
DOI: 10.4018/978-1-60566-766-9.ch016
Abstract
This chapter proposes the application of machine learning techniques, based on first-order logic as a representation language, to the real-world application domain of document processing. First, the tasks and problems involved in document processing are presented, along with the prototypical system DOMINUS and its architecture, whose components are aimed at facing these issues. Then, a closer look is provided for the learning component of the system, and the two sub-systems that are in charge of performing supervised and unsupervised learning as a support to the system performance. Finally, some experiments are reported that assess the quality of the learning performance. This is intended to prove to researchers and practitioners of the field that first-order logic learning can be a viable solution to tackle the domain complexity, and to solve problems such as incremental evolution of the document repository.