An Unsupervised Multimodal Semantic-Enhanced Entity Alignment Method Based on Large Language Models

Multimodal entity alignment identifies equivalent entities referring to the same real-world object across heterogeneous multimodal knowledge graphs and is essential for multi-source knowledge integration. Existing methods often rely on manually labeled seed alignments and multimodal feature similarity, limiting their ability to capture implicit semantic equivalence under heterogeneous attribute and relation expressions. Large language models (LLMs) offer strong semantic understanding and reasoning capabilities for unsupervised alignment, but their direct application remains challenging because of the large entity search space, redundant knowledge graph information, and the stochasticity of LLM inference. To address these challenges, we propose MEA-LLM, an LLM-based fully unsupervised multimodal semantic-enhanced entity alignment method. MEA-LLM first pre-screens candidate entities by jointly exploiting multimodal similarities derived from visual, attribute, and relational information. It then selects discriminative attributes and relations based on degree of freedom, candidate frequency, and discriminability for LLM reasoning. Multiple inference and result aggregation are further employed to improve robustness. Experiments on FB-DB15K and FB-YAGO15K show that MEA-LLM outperforms existing methods across multiple evaluation metrics. Further ablation studies, hyperparameter analyses, experiments with different model-scales, and efficiency evaluations demonstrate the effectiveness of the core components and indicate that MEA-LLM achieves a favorable trade-off between entity alignment performance and inference cost.