Probabilistic Inductive Logic Programming Theory and Application
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Subseries of Lecture Notes in Computer Science
4911
Luc De Raedt Paolo Frasconi Kristian Kersting Stephen Muggleton (Eds.)
Probabilistic Inductive Logic Programming Theory and Applications
13
Series Editors Jaime G. Carbonell, Carnegie Mellon University, Pittsburgh, PA, USA Jörg Siekmann, University of Saarland, Saarbrücken, Germany Volume Editors Luc De Raedt Katholieke Universiteit Leuven Department of Computer Science, Belgium E-mail: [email protected] Paolo Frasconi Università degli Studi di Firenze Machine Learning and Neural Networks Group, Dipartimento di Sistemi e Informatica, Italy E-mail: [email protected]fi.it Kristian Kersting Massachusetts Institute of Technology, CSAIL E-mail: [email protected] Stephen Muggleton Imperial College London, Department of Computing E-mail: [email protected]
Library of Congress Control Number: Applied for
CR Subject Classification (1998): I.2.3, I.2.6, I.2, D.1.6, F.4.1, J.3 LNCS Sublibrary: SL 7 – Artificial Intelligence ISSN ISBN-10 ISBN-13
0302-9743 3-540-78651-1 Springer Berlin Heidelberg New York 978-3-540-78651-1 Springer Berlin Heidelberg New York
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Preface
One of the key open questions within artificial intelligence is how to combine probability and logic with learning. This question is getting an increased attention in several disciplines such as knowledge representation, reasoning about uncertainty, data mining, and machine learning simulateously, resulting in the newly emerging subfield known as statistical relational learning and probabilistic inductive logic programming. A major driving force is the explosive growth in the amount of heterogeneous data that is being collected in the business and scientific world. Example domains include bioinformatics, chemoinformatics, transportation systems, communication networks, social network analysis, link analysis, robotics, among others. The structures encountered can be as simple as sequences and trees (such as those arising in protein secondary structure prediction and natural language parsing) or as complex as citation graphs, the World Wide Web, and relational databases. This book provides an introduction to this field with an emphasis on those m
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