Object Identification with Constraints

Abstract

Object identification aims at identifying different representations of the same object based on noisy attributes such as descriptions of the same product in different online shops or references to the same paper in different publications. Numerous solutions have been proposed for solving this task, almost all of them based on similarity functions of a pair of objects. Although today the similarity functions are learned from a set of labeled training data, the structural information given by the labeled data is not used. By formulating a generic model for object identification we show how almost any proposed identification model can easily be extended for satisfying structural constraints. Therefore we propose a model that uses structural information given as pairwise constraints to guide collective decisions about object identification in addition to a learned similarity measure. We show with empirical experiments on public and on real-life data that combining both structural information and attribute-based similarity enormously increases the overall performance for object identification tasks.