Learning Probabilistic Models of Relational Structure

TLDR

Most real‑world data is relational, yet conventional learning methods flatten it, losing structure; probabilistic relational models (PRMs) enable probabilistic modeling across multiple entities using their relations. This work proposes extending probabilistic modeling to the relational structure itself, not just attributes. The authors introduce reference uncertainty and existence uncertainty as mechanisms for structural uncertainty, detailing when each applies and providing learning algorithms. Experiments demonstrate that the learned models can predict relational structure, and incorporating observed structure enhances attribute prediction accuracy.

Abstract

Most real-world data is stored in relational form. In contrast, most statistical learning methods work with “flat” data representations, forcing us to convert our data into a form that loses much of the relational structure. The recently introduced framework of probabilistic relational models (PRMs) allows us to represent probabilistic models over multiple entities that utilize the relations between them. In this paper, we propose the use of probabilistic models not only for the attributes in a relational model, but for the relational structure itself. We propose two mechanisms for modeling structural uncertainty: reference uncertainty and existence uncertainty. We describe the appropriate conditions for using each model and present learning algorithms for each. We present experimental results showing that the learned models can be used to predict relational structure and, moreover, the observed relational structure can be used to provide better predictions for the attributes in the model.