Instance-based Learning for Knowledge Base Completion
This work addresses interpretability and performance in knowledge base completion for AI researchers, offering incremental improvements by integrating IBL with existing methods.
The paper tackles knowledge base completion by proposing instance-based learning (IBL), which uses prototype entities for interpretability and combines with translational models, achieving effectiveness in experiments. It also challenges the view that rule-based models rely on semantic compatibility, showing that IBL rules, though a small portion, outperform non-IBL rules across four benchmarks, providing new insights into model mechanisms.
In this paper, we propose a new method for knowledge base completion (KBC): instance-based learning (IBL). For example, to answer (Jill Biden, lived city,? ), instead of going directly to Washington D.C., our goal is to find Joe Biden, who has the same lived city as Jill Biden. Through prototype entities, IBL provides interpretability. We develop theories for modeling prototypes and combining IBL with translational models. Experiments on various tasks confirmed the IBL model's effectiveness and interpretability. In addition, IBL shed light on the mechanism of rule-based KBC models. Previous research has generally agreed that rule-based models provide rules with semantically compatible premises and hypotheses. We challenge this view. We begin by demonstrating that some logical rules represent {\it instance-based equivalence} (i.e. prototypes) rather than semantic compatibility. These are denoted as {\it IBL rules}. Surprisingly, despite occupying only a small portion of the rule space, IBL rules outperform non-IBL rules in all four benchmarks. We use a variety of experiments to demonstrate that rule-based models work because they have the ability to represent instance-based equivalence via IBL rules. The findings provide new insights of how rule-based models work and how to interpret their rules.