Inference of abduction theories for handling incompleteness in first-order learning |
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Authors: | F Esposito S Ferilli T M A Basile N Di Mauro |
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Affiliation: | (1) Department of Computer Science, University of Bari, Bari, Italy |
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Abstract: | In real-life domains, learning systems often have to deal with various kinds of imperfections in data such as noise, incompleteness
and inexactness. This problem seriously affects the knowledge discovery process, specifically in the case of traditional Machine
Learning approaches that exploit simple or constrained knowledge representations and are based on single inference mechanisms.
Indeed, this limits their capability of discovering fundamental knowledge in those situations. In order to broaden the investigation
and the applicability of machine learning schemes in such particular situations, it is necessary to move on to more expressive
representations which require more complex inference mechanisms. However, the applicability of such new and complex inference
mechanisms, such as abductive reasoning, strongly relies on a deep background knowledge about the specific application domain.
This work aims at automatically discovering the meta-knowledge needed to abduction inference strategy to complete the incoming
information in order to handle cases of missing knowledge.
Floriana Esposito received the Laurea degree in electronic Physics from the University of Bari, Italy, in 1970. Since 1994 is Full Professor
of Computer Science at the University of Bari and Dean of the Faculty of Computer Science from 1997 to 2002. She founded and
chairs the Laboratory for Knowledge Acquisition and Machine Learning of the Department of Computer Science. Her research activity
started in the field of numerical models and statistical pattern recognition. Then her interests moved to the field of Artificial
Intelligence and Machine Learning. The current research concerns the logical and algebraic foundations of numerical and symbolic
methods in machine learning with the aim of the integration, the computational models of incremental and multistrategy learning,
the revision of logical theories, the knowledge discovery in data bases. Application include document classification and understanding,
content based document retrieval, map interpretation and Semantic Web. She is author of more than 270 scientific papers and
is in the scientific committees of many international scientific Conferences in the field of Artificial Intelligence and Machine
Learning. She co-chaired ICML96, MSL98, ECML-PKDD 2003, IEA-AIE 2005, ISMIS 2006.
Stefano Ferilli was born in 1972. After receiving his Laurea degree in Information Science in 1996, he got a Ph.D. in Computer Science at
the University of Bari in 2001. Since 2002 he is an Assistant Professor at the Department of Computer Science of the University
of Bari. His research interests are centered on Logic and Algebraic Foundations of Machine Learning, Inductive Logic Programming,
Theory Revision, Multi-Strategy Learning, Knowledge Representation, Electronic Document Processing and Digital Libraries.
He participated in various National and European (ESPRIT and IST) projects concerning these topics, and is a (co-)author of
more than 80 papers published on National and International journals, books and conferences/workshops proceedings.
Teresa M.A. Basile got the Laurea degree in Computer Science at the University of Bari, Italy (2001). In March 2005 she discussed a Ph.D. thesis
in Computer Science at the University of Bari titled “A Multistrategy Framework for First-Order Rules Learning.” Since April
2005, she is a research at the Computer Science Department of the University of Bari working on methods and techniques of
machine learning for the Semantic Web. Her research interests concern the investigation of symbolic machine learning techniques,
in particular of the cooperation of different inferences strategies in an incremental learning framework, and their application
to document classification and understanding based on their semantic. She is author of about 40 papers published on National
and International journals and conferences/workshops proceedings and was/is involved in various National and European projects.
Nicola Di Mauro got the Laurea degree in Computer Science at the University of Bari, Italy. From 2001 he went on making research on machine
learning in the Knowledge Acquisition and Machine Learning Laboratory (LACAM) at the Department of Computer Science, University
of Bari. In March 2005 he discussed a Ph.D. thesis in Computer Science at the University of Bari titled “First Order Incremental
Theory Refinement” which faces the problem of Incremental Learning in ILP. Since January 2005, he is an assistant professor
at the Department of Computer Science, University of Bari. His research activities concern Inductive Logic Programming (ILP),
Theory Revision and Incremental Learning, Multistrategy Learning, with application to Automatic Document Processing. On such
topics HE is author of about 40 scientific papers accepted for presentation and publication on international and national
journals and conference proceedings. He took part to the European projects 6th FP IP-507173 VIKEF (Virtual Information and
Knowledge Environment Framework) and IST-1999-20882 COLLATE (Collaboratory for Annotation, Indexing and Retrieval of Digitized
Historical Archive Materials), and to various national projects co-funded by the Italian Ministry for the University and Scientific
Research. |
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Keywords: | Incomplete knowledge Inductive Logic Programming Abduction |
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