PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Analogical Reasoning with Relational Bayesian Sets
Ricardo Silva, Katherine Heller and Zoubin Ghahramani
In: AISTATS 2007, Puerto Rico(2007).

Abstract

Analogical reasoning depends fundamentally on the ability to learn and generalize about relations between objects. There are many ways in which objects can be related, making automated analogical reasoning very chal- lenging. Here we develop an approach which, given a set of pairs of related objects S = {A1:B1,A2:B2,...,AN:BN}, measures how well other pairs A:B fit in with the set S. This addresses the question: is the relation between objects A and B analogous to those relations found in S? We recast this classi- cal problem as a problem of Bayesian analy- sis of relational data. This problem is non- trivial because direct similarity between ob- jects is not a good way of measuring analo- gies. For instance, the analogy between an electron around the nucleus of an atom and a planet around the Sun is hardly justified by isolated, non-relational, comparisons of an electron to a planet, and a nucleus to the Sun. We develop a generative model for predicting the existence of relationships and extend the framework of Ghahramani and Heller (2005) to provide a Bayesian measure for how anal- ogous a relation is to other relations. This sheds new light on an old problem, which we motivate and illustrate through practical ap- plications in exploratory data analysis.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:6732
Deposited By:Katherine Heller
Deposited On:08 March 2010