Abstract
By using the principle of maximum entropy incomplete probabilistic knowledge can be completed to a full joint distribution. This inductive knowledge representation method can be reversed to extract probabilistic rules from an empirical probability distribution. Based on this idea propositional learning approach has been developed. Recently, an extension to a relational language has been presented, where, however, a central aspect, finding and resolving algebraic equations needed for the solution, has been treated as a black box. Here, we investigate both problems in more detail. We explain how equations for relational knowledge bases can be resolved, and give a comprehensive example of computing a relational knowledge base from a probability distribution. Furthermore, we describe how propositional mechanisms for finding equations can be refined to focus on more interesting equations and to reduce the number of candidates.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
DeFinetti, B.: Theory of Probability, vol. 1,2. John Wiley and Sons, New York (1974)
Fisseler, J.: Learning and Modeling with Probabilistic Conditional Logic. Dissertations in Artificial Intelligence, vol. 328. IOS Press, Amsterdam (2010)
Getoor, L., Taskar, B. (eds.): Introduction to Statistical Relational Learning. MIT Press (2007)
Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques. Morgan Kaufmann (2011)
Kern-Isberner, G.: Conditionals in Nonmonotonic Reasoning and Belief Revision. LNCS (LNAI), vol. 2087. Springer, Heidelberg (2001)
Kern-Isberner, G., Fisseler, J.: Knowledge discovery by reversing inductive knowledge representation. In: Proceedings of the Ninth International Conference on the Principles of Knowledge Representation and Reasoning, KR 2004, pp. 34–44. AAAI Press (2004)
Paris, J.B.: The uncertain reasoner’s companion – A mathematical perspective. Cambridge University Press (1994)
Potyka, N., Beierle, C.: An approach to learning relational probabilistic FO-PCL knowledge bases. In: Hüllermeier, E., Link, S., Fober, T., Seeger, B. (eds.) SUM 2012. LNCS (LNAI), vol. 7520, pp. 625–632. Springer, Heidelberg (2012)
De Raedt, L., Blockeel, H., Dehaspe, L., Van Laer, W.: Three companions for data mining in first order logic. In: Relational Data Mining, pp. 105–139. Springer (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Potyka, N., Beierle, C., Kern-Isberner, G. (2013). On the Problem of Reversing Relational Inductive Knowledge Representation. In: van der Gaag, L.C. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2013. Lecture Notes in Computer Science(), vol 7958. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39091-3_41
Download citation
DOI: https://doi.org/10.1007/978-3-642-39091-3_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39090-6
Online ISBN: 978-3-642-39091-3
eBook Packages: Computer ScienceComputer Science (R0)