PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Ranking relations using analogies in biological and information networks
Ricardo Silva, Katherine Heller, Zoubin Ghahramani and E M Airoldi
Annals of Applied Statistics Volume 4, Number 2, pp. 615-644, 2010.

Abstract

Analogical reasoning depends fundamentally on the ability to learn and generalize about relations between objects. We develop an approach to rela- tional learning which, given a set of pairs of objects S = {A(1) : B (1) , A(2) : B(2),...,A(N) :B(N)}, measures how well other pairs A:B fit in with the set S. Our work addresses the following question: is the relation between objects A and B analogous to those relations found in S? Such questions are particularly relevant in information retrieval, where an investigator might want to search for analogous pairs of objects that match the query set of inter- est. There are many ways in which objects can be related, making the task of measuring analogies very challenging. Our approach combines a similarity measure on function spaces with Bayesian analysis to produce a ranking. It requires data containing features of the objects of interest and a link matrix specifying which relationships exist; no further attributes of such relation- ships are necessary. We illustrate the potential of our method on text analysis and information networks. An application on discovering functional interac- tions between pairs of proteins is discussed in detail, where we show that our approach can work in practice even if a small set of protein pairs is provided.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:7857
Deposited By:Zoubin Ghahramani
Deposited On:17 March 2011