FOL Learning for Knowledge Discovery in Documents-Reference-Cited by-同舟云学术

FOL Learning for Knowledge Discovery in Documents

Published:2012 Issue: Volume: Page:1237-1262
ISSN:
Container-title:Machine Learning
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Author:

Ferilli Stefano¹,Esposito Floriana¹,Biba Marenglen¹,Basile Teresa M.A.¹,Di Mauro Nicola¹

Affiliation:

1. Università degli Studi di Bari, Italy

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.

Publisher

IGI Global

Reference65 articles.

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