E asy EA : Large Language Model is All You Need in Entity Alignment Between Knowledge Graphs (2025)

Abstract: Entity alignment (EA) aims to identify entities in different knowledge graphs (KGs) that represent the same real-world objects. Traditional EA methods typically embed entity information into vector space under the guidance of seed entity pairs, and align entities by calculating and comparing the similarity between entity embeddings. With the advent of large language models (LLMs), emerging methods are increasingly integrating LLMs with traditional methods to leverage external knowledge and improve EA accuracy. However, this integration also introduces additional computational complexity and operational overhead, and still requires seed pairs that are scarce and expensive to obtain. To address these challenges, we propose EasyEA, the first end-to-end EA framework based on LLMs that requires no training. EasyEA consists of three main stages: (1) Information Summarization, (2) Embedding and Feature Fusion, and (3) Candidate Selection. By automating the EA process, EasyEA significantly reduces the reliance on seed entity pairs while demonstrating superior performance across various datasets, covering crosslingual, sparse, large-scale, and heterogeneous scenarios. Extensive experimental results show that EasyEA not only simplifies the EA process but also achieves state-of-the-art (SOTA) performance on diverse datasets, providing a promising solution for advancing EA tasks.

## introduction knowledge graphs (kgs) are structured knowledge bases widely used in tasks such as semantic search, recommendation systems, and question answering. these graphs typically represent real-world objects and their relation in the form of triples (entityrelation-entity or entity-attribute-value) @xcite . the goal of entity alignment (ea) is to identify equivalent entity pairs across different kgs that refer to the same real-world object @xcite . as kgs differ in language, structure, and schema, ea has become a challenging task @xcite . traditional ea methods, such as translationbased methods, machine learning-based methods, and graph neural network (gnn)-based methods @xcite , rely on symbolic and structural features to align entities across kgs. these methods perform well in scenarios with consistent naming conventions or rich relation structures @xcite . however, when applied to large or diverse kgs, they face significant challenges, particularly due to linguistic and structural heterogeneity. furthermore, these methods require large amounts of labeled data for training and fail to incorporate external knowledge, both of which are crucial for accurate ea @xcite @xcite . additionally, the black-box nature of embedding similarity calculations limits their interpretability and reduces adaptability to complex ea scenarios @xcite . llms have significantly advanced various fields with their exceptional semantic understanding, contextual inference, and cross-lingual capabilities. these strengths make them particularly valuable for tackling challenges in ea, such as bridging the semantic gap between kgs and enriching limited entity knowledge. recent ea methods combining llms with traditional methods have led to notable improvements in performance. some methods focus on turning entity information into a common semantic form and using the search abilities of llms to align them efficiently, such as dera @xcite and seg-align @xcite . others leverage the reasoning power of llms to improve alignment accuracy and robustness through methods like multi-step reasoning and active learning, such as chatea @xcite and llmea @xcite . while this combination enhances the accuracy of ea, it also introduces significant resource overhead. specifically, methods that utilize llms require not only the computational resources necessary for smaller models but also the additional resources required by the llm itself. this creates a challenge in balancing resource consumption with alignment accuracy @xcite . furthermore, these methods rely on entity names to supplement or enrich entity information using llms, which can lead to potential data leakage @xcite , where sensitive or proprietary data associated with the entities may unintentionally influence the alignment process. to cope with the complexity of the current ea task, we propose easyea, an efficient ea framework driven entirely by llms, aimed at overcoming the limitations of traditional models and hybrid models. easyea consists of three key stages: (1) information summarization. at this stage, we focus on using llms to extract semantic information from the kg data. the llm summarizes the key attributes and relations of entities to capture their core semantic meanings. (2) embedding and feature fusion. in this stage, we embed the summaries using llms and integrate the diverse feature embeddings obtained to construct a holistic and enriched representation of entities. (3) candidate selection. we propose a hierarchical strategy, which leverages multiple views of information, enabling the llm to more accurately select the most appropriate target entities, thereby enhancing the accuracy and reliability of ea. through extensive experiments on multiple datasets, easyea demonstrates excellent performance, surpassing existing state-of-the-art (sota) models. unlike traditional methods, easyea eliminates the need for manual seed entity pair construction and additional model training, significantly improving efficiency while ensuring high-quality ea results. the main contributions of our framework are: dwy, demonstrating its effectiveness and robustness in challenging scenarios such as cross-lingual alignment, large-scale kgs, heterogeneous kgs, and sparse datasets. 2 related works ea methods can generally be classified into four categories: translation-based methods, machine learning-based methods, gnn-based methods, and llm-enhanced methods. translation-based methods. translationbased methods, such as mtranse @xcite , bootea @xcite , and transedge @xcite , represent entities and relations in a low-dimensional vector space. in these models, a relation in kgs is treated as a translation mapping the head entity vector to the tail entity vector @xcite . these methods align entities by minimizing the distance between the vectors of aligned entities. while effective in homogeneous kgs, these methods face challenges in more complex or heterogeneous graph structures, where relations can be more complicated @xcite . furthermore, translation-based models often struggle with cross-lingual or sparse data settings, where the embeddings may fail to fully capture the diversity and complexity of the data. ## problem definition a knowledge graph (kg) is represented as @xmath0, r, a, v, t ), where v , r, a, v, and t represent entities, relations, attribute types, attribute values, and triples, respectively. each entity @xmath1, and each relation @xmath2. the set of attribute types is denoted as a, and the set of attribute values is denoted as v. the set of triples t can be further divided into two categories: relation triples and attribute triples. relation triples are represented as ## method in this section, we describe the core process of the easyea framework, which is divided into three main stages: (1) information summarization, (2) embedding and feature fusion, and (3) candidate selection. the framework diagram, shown in figure 1 , illustrates the overall process of these stages. ## conclusion this work primarily explores the feasibility of using llms for ea without relying on traditional models. we propose the easyea framework, which relies solely on llms for ea, and validate its feasibility through extensive experiments and ablation analysis, achieving excellent alignment results. this method eliminates the training requirements of traditional models and the need for seed entity pair construction, making ea simpler and more efficient. additionally, we evaluate easyea's performance on multiple common datasets, achieving strong results, and introduce a simple and efficient candidate selection method to further enhance ea efficiency.