Growth Patterns of Inference

TL;DR

This paper investigates how the structure of search spaces influences inference efficiency, revealing that uniform spaces favor larger knowledge bases while skewed distributions excel in smaller ones, guiding knowledge acquisition strategies.

Contribution

It introduces a model linking ground fact distribution to inference performance, providing insights for optimizing search space design in knowledge systems.

Findings

Uniform search spaces are better for large knowledge bases.

Skewed degree distributions improve performance in smaller KBs.

Structural analysis can guide fact acquisition in learning systems.

Abstract

What properties of a first-order search space support/hinder inference? What kinds of facts would be most effective to learn? Answering these questions is essential for understanding the dynamics of deductive reasoning and creating large-scale knowledge-based learning systems that support efficient inference. We address these questions by developing a model of how the distribution of ground facts affects inference performance in search spaces. Experiments suggest that uniform search spaces are suitable for larger KBs whereas search spaces with skewed degree distribution show better performance in smaller KBs. A sharp transition in Q/A performance is seen in some cases, suggesting that analysis of the structure of search spaces with existing knowledge should be used to guide the acquisition of new ground facts in learning systems.