Learning Rules from KGs Guided by Language Models

TL;DR

This paper investigates how language models can enhance rule learning from knowledge graphs, addressing challenges in rule ranking over incomplete or biased KGs, and compares LM-based methods with traditional approaches.

Contribution

It introduces a framework for leveraging language models to improve rule learning and ranking in knowledge graphs, evaluating their effectiveness against existing methods.

Findings

Language models can improve rule ranking accuracy.

LM-guided rule learning outperforms traditional statistical methods.

Incorporating LMs reduces bias in rule extraction.

Abstract

Advances in information extraction have enabled the automatic construction of large knowledge graphs (e.g., Yago, Wikidata or Google KG), which are widely used in many applications like semantic search or data analytics. However, due to their semi-automatic construction, KGs are often incomplete. Rule learning methods, concerned with the extraction of frequent patterns from KGs and casting them into rules, can be applied to predict potentially missing facts. A crucial step in this process is rule ranking. Ranking of rules is especially challenging over highly incomplete or biased KGs (e.g., KGs predominantly storing facts about famous people), as in this case biased rules might fit the data best and be ranked at the top based on standard statistical metrics like rule confidence. To address this issue, prior works proposed to rank rules not only relying on the original KG but also facts predicted by a KG embedding model. At the same time, with the recent rise of Language Models (LMs), several works have claimed that LMs can be used as alternative means for KG completion. In this work, our goal is to verify to which extent the exploitation of LMs is helpful for improving the quality of rule learning systems.