Two Heads Are Better Than One: Integrating Knowledge from Knowledge
  Graphs and Large Language Models for Entity Alignment
Linyao Yang Hongyang Chen Xiao Wang Jing Yang Fei-Yue Wang Han Liu
Entity alignment, which is a prerequisite for creating a more comprehensive
Knowledge Graph (KG), involves pinpointing equivalent entities across disparate
KGs. Contemporary methods for entity alignment have predominantly utilized
knowledge embedding models to procure entity embeddings that encapsulate
various similarities-structural, relational, and attributive. These embeddings
are then integrated through attention-based information fusion mechanisms.
Despite this progress, effectively harnessing multifaceted information remains
challenging due to inherent heterogeneity. Moreover, while Large Language
Models (LLMs) have exhibited exceptional performance across diverse downstream
tasks by implicitly capturing entity semantics, this implicit knowledge has yet
to be exploited for entity alignment. In this study, we propose a Large
Language Model-enhanced Entity Alignment framework (LLMEA), integrating
structural knowledge from KGs with semantic knowledge from LLMs to enhance
entity alignment. Specifically, LLMEA identifies candidate alignments for a
given entity by considering both embedding similarities between entities across
KGs and edit distances to a virtual equivalent entity. It then engages an LLM
iteratively, posing multiple multi-choice questions to draw upon the LLM's
inference capability. The final prediction of the equivalent entity is derived
from the LLM's output. Experiments conducted on three public datasets reveal
that LLMEA surpasses leading baseline models. Additional ablation studies
underscore the efficacy of our proposed framework.