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.

本研究提出了一个名为 IBL 的知识库补全方法，通过原型实体提供可解释性。IBL 规则不同于语义兼容的规则，实验表明 IBL 规则的效果优于非 IBL 规则。