论文标题
基于实例的学习知识基础完成
Instance-based Learning for Knowledge Base Completion
论文作者
论文摘要
在本文中，我们提出了一种新的知识基础完成方法（KBC）：基于实例的学习（IBL）。例如，要回答（吉尔·拜登（Jill Biden），居住城市，？），而不是直接去华盛顿特区，我们的目标是找到与吉尔·拜登（Jill Biden）相同的城市乔·拜登（Joe Biden）。通过原型实体，IBL提供了解释性。我们开发了建模原型并将IBL与翻译模型相结合的理论。有关各种任务的实验证实了IBL模型的有效性和解释性。 此外，IBL阐明了基于规则的KBC模型的机制。先前的研究通常同意，基于规则的模型提供了具有语义上兼容前提和假设的规则。我们挑战这种观点。我们首先证明某些逻辑规则代表{\ IT基于实例的等价}（即原型）而不是语义兼容性。这些表示为{\ it IBL规则}。令人惊讶的是，尽管仅占据了规则空间的一小部分，但IBL在所有四个基准测试中都规定了超越非IBL规则。我们使用各种实验来证明基于规则的模型起作用，因为它们具有通过IBL规则表示基于实例的等价性的能力。这些发现提供了有关基于规则的模型如何工作以及如何解释其规则的新见解。
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.
