Unlocking the Power of Large Language Models for Entity Alignment (2024)

Abstract: Entity Alignment (EA) is vital for integrating diverse knowledge graph (KG) data, playing a crucial role in data-driven AI applications. Traditional EA methods primarily rely on comparing entity embeddings, but their effectiveness is constrained by the limited input KG data and the capabilities of the representation learning techniques. Against this backdrop, we introduce ChatEA, an innovative framework that incorporates large language models (LLMs) to improve EA. To address the constraints of limited input KG data, ChatEA introduces a KG-code translation module that translates KG structures into a format understandable by LLMs, thereby allowing LLMs to utilize their extensive background knowledge to improve EA accuracy. To overcome the over-reliance on entity embedding comparisons, ChatEA implements a two-stage EA strategy that capitalizes on LLMs’ capability for multi-step reasoning in a dialogue format, thereby enhancing accuracy while preserving efficiency. Our experimental results affirm ChatEA’s superior performance, highlighting LLMs’ potential in facilitating EA tasks.The source code is available at https://anonymous.4open.science/r/ChatEA/.

## introduction entity alignment (ea) aims at aligning entities from diverse knowledge graphs (kgs). it is a pivotal step in unifying data from heterogeneous sources and plays a crucial role in data-driven ai. current ea methods predominantly rely on measuring the similarity of entity embeddings derived from knowledge representation learning (krl) techniques. these techniques learn from the topology and semantics of kgs to derive entity embeddings @xcite . however, these methods fail to incorporate the external knowledge of entities, which is essential for the alignment process. moreover, the krl-based alignment methods merely calculate the similarity between two entity embeddings in a black-box manner, which lacks a detailed and explicit reasoning process for alignment. such limitations significantly affect the performance of ea methods, especially in aligning highly heterogeneous kg pairs where krl-based methods struggle to capture the complex correlations among kgs @xcite . recently, large language models (llms) have showcased their effectiveness across a range of natural language processing tasks, revealing a vast but under-explored potential in ea. these llms are trained on extensive corpora to encapsulate external knowledge, offering a rich source of contextual information for entities in kgs @xcite . furthermore, the recent studies of adopting llms in knowledge extraction and reasoning also demonstrated their strong reasoning abilities on kgs @xcite . these features of llms offer a promising path to overcome the constraints faced by current ea methods. in this paper, we propose chatea, a novel framework designed to enhance krl-based ea methods by utilizing the extensive background knowledge and reasoning abilities of llms. as shown in figure 1 , chatea integrates krl-based ea methods in the feature pre-processing phase to assist llms in the subsequent selection of candidate entities. to overcome the constraints of limited input kg data, chatea firstly features a kg-code translation mod-ule. the module initially converts kgs into a code format, explicitly accounting for entity definition toward llms' comprehension of graph-structured kg data @xcite . then it facilitates the generation of entity descriptions leveraging llms' background knowledge. to overcome the over-reliance on comparing entity embeddings for ea and improve transparency, chatea employs a two-stage ea strategy, leveraging llms' multi-step reasoning in dialogue form to enhance accuracy and maintain efficiency. during the candidate collecting stage, chatea identifies potential entities by comparing embeddings derived from the earlier feature pre-processing phase. in the reasoning and rethinking stage, it evaluates the likelihood of alignment between entity pairs by comprehensively considering the name, structure, entity description, and temporal information, and then decides whether to broaden the search scope and continue subsequent iterations. we validated our method on two conventional ea datasets: dbp15k(en-fr) and dbp-wiki, along with two more challenging but practical datasets: icews-wiki and icews-yago, characterized by their highly heterogeneous kgs and the complexity of capturing inter-kg correlations @xcite . the extensive experiments reveal chatea's superiority over existing state-of-the-art ea methods and underscore the potential of llms in enhancing ea performance. notably, chatea significantly improves hits@1 by 9%-16% compared to previous state-of-the-art methods on the two challenging datasets. in general, our main contributions are as follows: ## preliminaries and related works this section first delineates the relevant definitions, followed by an overview of related works. ## method in this section, we present chatea, a novel framework that unlocks the power of llms for ea tasks. we meticulously design the architecture of chatea around three pivotal objectives: ## experiments in this section, we evaluate the chatea to ascertain its effectiveness in ea tasks. our investigation is guided by three pivotal research questions: this examination assesses whether chatea manages to strike an optimal balance between high accuracy and computational efficiency, a crucial aspect of its practical application. ## conclusion in this paper, we focus on harnessing the capabilities of llms for ea, leading to the development of chatea. this innovative framework is tailored to address three pivotal challenges: (1) enhancing llms' ability to interpret and understand kgs, (2) leveraging the inherent knowledge within llms for more effective ea, and (3) improving the efficiency of llms in ea contexts. our comprehensive experiments, conducted across four representative datasets, underscore chatea's superiority, particularly in applications requiring high precision in ea. these findings further illuminate the significant potential of llms in ea tasks for explorations.