AI 中文总结
研究实体对齐问题，传统方法语义理解不足，基于LLM的方法未形成稳定身份空间。提出IRIS框架，从冻结LLM提取上下文表示构建实体签名，形成共享空间实现跨图谱对齐，在多个基准测试中取得良好成绩。
AI 中文摘要
实体对齐（EA）旨在跨知识图谱识别指同一现实世界对象的实体。传统EA方法主要利用显式图结构和文本字段，语义理解不足。现有基于大语言模型（LLM）的EA方法多将其用于辅助生成或候选条件决策，未形成稳定可比的身份空间。为此提出IRIS，一个无需训练的框架，从冻结LLM中提取面向身份的上下文表示，为每个实体构建类似虹膜的签名，形成共享空间，通过直接相似性比较实现跨不同知识图谱对齐。在四个基准测试中，最佳IRIS变体在不同数据集上取得了高Hits@1分数。
英文摘要
Entity alignment (EA) identifies entities across knowledge graphs (KGs) that refer to the same real-world object. Conventional EA methods mainly exploit explicit graph structures and textual fields, which often provide insufficient semantic understanding to recognize the same entity under heterogeneous descriptions and distinguish it from semantically similar entities. Although large language models (LLMs) offer deeper entity understanding, existing LLM-based EA methods largely use this capability for auxiliary generation or candidate-conditioned decisions. Consequently, such understanding is not distilled into a stable and directly comparable identity space, leaving alignment tied to specific KG pairs or candidate sets and requiring repeated processing as the matching context changes. To address these limitations, we propose IRIS (Identity Representations from Internal States), a training-free framework that constructs for each entity an iris-like signature encoding its distinctive and stable identity characteristics. IRIS derives these signatures by eliciting identity-oriented contextual representations from a frozen LLM, thereby forming a shared space in which each entity is encoded once and can be aligned across different KGs through direct similarity comparison, without pair-dependent representation construction or candidate-wise LLM inference. Across four established EA benchmarks and two frozen LLM backbones, the best IRIS variants achieve Hits@1 scores of 100.00, 99.38, 98.31, and 97.99 on D-Y-15K V2, DBP-WIKI, ICEWS-WIKI, and ICEWS-YAGO, respectively.
Comments9 pages, 1 figure, 3 tables
