Entity Profile Generation and Reasoning with LLM s for Entity Alignment (2025)

Abstract: Entity alignment (EA) involves identifying and linking equivalent entities across different knowledge graphs (KGs). While knowledge graphs provide structured information about real-world entities, only a small fraction of these entities are aligned. The entity alignment process is challenging due to heterogeneity in KGs, such as differences in structure, terminology, and attribute details. Traditional EA methods use multi-aspect entity embeddings to align entities. Although these methods perform well in certain scenarios, their effective- ness is often constrained by sparse or incomplete data in knowledge graphs and the limitations of embedding techniques. We propose ProLEA ( Profile Generation and Reasoning with LLMs for Entity Alignment) an entity alignment method that combines large language models (LLMs) with entity embed- dings. LLMs generate contextual profiles for entities based on their properties. Candidate entities identified by entity embedding techniques are then re-evaluated by the LLMs, using its background knowledge and the generated profile. A thresholding mechanism is introduced to resolve conflicts between LLMs predictions and embedding-based alignments. This method enhances alignment accuracy, robustness, and explainability, particularly for complex, het- erogeneous knowledge graphs. Furthermore, ProLEA is a generalized framework. Its profile generation and LLM-enhanced entity align- ment components can improve the performance of existing entity alignment models.

## introduction entity alignment is the task of integrating heterogeneous knowledge among different knowledge graphs (kgs). knowledge graph (kg) is a popular way of storing facts about real-world entities. traditional ea methods mainly rely on measuring the similarity of entity embeddings derived from knowledge representation learning. these meth-ods, however, do not consider external knowledge, limiting their ability to align entities accurately, particularly in heterogeneous kgs. recently, large language models have shown great potential in enhancing ea tasks. llms are trained on vast data and can provide valuable external knowledge about entities, which can improve alignment accuracy. additionally, their strong reasoning abilities have been demonstrated in knowledge extraction and reasoning tasks. studies such as chatea @xcite ) and llmea @xcite have explored integrating llms into ea. chatea uses llms for iterative reasoning and incorporates diverse data types (names, descriptions, temporal data), while llmea leverages entity structure and name embeddings for candidate selection, refining the final alignments through llms' reasoning. these approaches show the promising role of llms in improving ea methods. in this paper, we propose prolea framework that leverages large language models to generate a summarized entity profile. these profiles integrate all available properties of an entity, including its attributes, relationships, and contextual information, into a coherent and structured representation. this profile acts as a reference for evaluating potential alignments. candidate entities are initially identified using entity embedding learning techniques, which generate an alignment shortlist based on similarity metrics. these candidates are then re-evaluated by the large language model (llm), utilizing its background knowledge and the generated entity profile to ensure precision. we introduce a thresholding mechanism to resolve conflicts between the predictions of large language models and candidate alignments generated by the embedding module. if the llm confirms a candidate, it is accepted as the final alignment. otherwise, llm generates a confidence score for its prediction. when the llm is highly confident that the candidate is incorrect, the alignment is rejected. however, if the confidence is low, indicating uncertainty, the original candidate alignment is retained. this approach ensures that only the most reliable alignments are selected, while maintaining the alignment process's accuracy, and explainability, particularly in the case of complex and heterogeneous knowledge graphs. overall, our contributions are summarized as follows: (1) we introduce llm-generated profiles combining attributes, relationships, and context for precise entity alignment; (2) combine embedding-based candidate generation with llm refinement for improved alignment accuracy; (3) develop a thresholding mechanism for resolving conflicts between embeddings and llm predictions based on confidence scores; (4) improve explainability and robustness for complex knowledge graphs, balancing accuracy and adaptability through llm-guided reasoning for reliable alignments; (5) we show that prolea functions as a modular post-processing component that enhances the performance of existing ea models @xcite without requiring changes to their original architectures. ## related works over the years, numerous approaches have been developed, each employing distinct methodologies to tackle the complexities of entity alignment effectively. in this section, we introduce the related work from various aspects. translation-based methods represent entities and relations as low-dimensional vectors in a shared space, modeling relations as translations between entity embeddings @xcite @xcite . models like mtranse @xcite , bootea @xcite , jape @xcite , kecg @xcite , transedge @xcite , jarka @xcite , embed entities and relations into a shared vector space to map entities across different knowledge graphs. these methods rely on translation-based knowledge graph embedding techniques to map entities across different knowledge graphs by constraining entity embeddings into a fixed distribution, facilitating cross-kg alignment. gnn based models like gcn-align @xcite and, rdgcn @xcite leverage gnns to integrate structural similarity and local graph neighborhoods, achieving comparable results. yang et al. @xcite @xcite ) also explore gnn-based and unsupervised approaches for entity alignment. bert-int @xcite , jsae (munne and ichise, 2020) and easae (munne and ichise, 2023) employ pre-trained language models to capture semantic richness from entity descriptions, while multike @xcite , tea @xcite and sdea @xcite utilize multiview information for alignment. recent studies, such as simple-hhea @xcite ) uses structured embeddings with logical reasoning. au-toalign @xcite leverages large language models for fully automatic kg alignment, improving semantic matching without manual supervision, while chen et al. @xcite address noisy llm-generated annotations to enhance robust entity alignment. chatea @xcite integrates llms for iterative reasoning to filter candidates, leveraging names, descriptions, and structures and llmea @xcite integrates multiple similarity features with llmbased refinement. in contrast, our proposed pro-lea model improves alignment accuracy, robustness, and explainability by addressing modularity, data sparsity, and generalization challenges in entity alignment through llm-driven profiling and reasoning. ## preliminary in this section, we formally define the terms used in this paper and the problem as well. definition 1 knowledge graph (kg): a knowledge graph @xmath0, r, t) , where e, r and t are the sets of entities, relations, and triples, respectively. definition 2 relational triples: t ⊂ e×r×e is a set of relational triples representing the relations between entities, where e and r are the sets of all entities and relations respectively. definition 3 attribute triples: a t ⊂ e ×a×l is a set of attribute triples representing the attributes of entities, where a is the set of all attributes, and each attribute a @xmath1. definition 4 given two kgs, kg 1 and kg 2 , the entity alignment problem aims to find all pairs like (e 1 , e 2 ) where e 1 ∈ kg 1 , e 2 ∈ kg 2 , and e 1 , e 2 represent the same real-world entity. given two knowledge graphs, our objective is to jointly learn their structure, attributes, and descriptions to identify all entity pairs that refer to the same real-world entity. in our paper, we use bold lowercase letters to represent embedding vectors and bold uppercase letters to denote matrices. ## model overview the overall architecture of prolea is shown in figure 1 . this framework comprises three distinct components: (1) entity profile generation, (2) embedding learning and, (3) llm-enhanced entity alignment. ## experiment in this section, we evaluate prolea to assess its effectiveness in entity alignment tasks. our investigation is guided by four key research questions: ## conclusion in conclusion, we propose an entity alignment method that combines large language models with entity embeddings to address challenges in heterogeneous knowledge graphs. llms generate contextual profiles for entities, which are re-evaluated alongside embeddings to refine candidate alignments. a thresholding mechanism resolves conflicts between llm and embedding predictions, enhancing alignment accuracy. this approach proves effective for complex or incomplete knowledge graphs, where traditional methods often struggle. moreover, it introduces a modular framework that enhances existing ea models through profile generation and prompt-based llm reasoning, without requiring any architectural modifications. extensive experiments demonstrate the model's flexibility and consistent performance gains across diverse datasets, making it practical and effective for largescale, real-world entity alignment tasks.