LOKI：用文本证据发现异构数据表间关联的新方法
Discovery-Driven Integration of Disjoint Tables via Text
数据集成领域的新论文，LOKI 能自动从文本里发现不同表之间的行级关联，精度 0.982，比直接调 LLM 便宜 40 倍，做数据湖的可以看看。
arXiv 论文提出 Text-Mediated Join Path Discovery 任务：数据湖中语义相关但缺少可连接属性的表格，需要靠非结构化文本中的句子来建立行级关联。作者提出 LOKI 架构，通过水平双向交叉注意力和全局表-文本对比目标，让行与句子的细粒度关联无需局部标注即可涌现。LOKI 会将隐式关联转化为可解释的连接路径，并物化为带句子级来源的强类型集成表。在真实世界基准上，LOKI 的 macro typed-pair 精度达到 0.982，LLM API 成本比直接提示低最多 40 倍。
Discovery-Driven Integration of Disjoint Tables via Text
Integrating heterogeneous datasets within data lakes is a critical challenge, particularly for semantically related tables that lack the explicit attributes needed to be joined. We study Discovery-Driven Integration, where the relevant sources and their missing relational structure must be discovered before integration. In this setting, unstructured text provides the evidence that connects otherwise disjoint tables. The fundamental challenge is to discover the relationships at a fine-grained level that connect individual rows from different tables through specific sentences. We formalize this task as Text-Mediated Join Path Discovery and propose a horizontal bidirectional cross-attention architecture called LOKI Latent-space Optimization for Knowledge Integration) that learns contextualized representations of table rows and sentences. Through a global table-text contrastive objective, fine-grained row-sentence associations emerge without explicit local supervision. Existing multi-modal discovery methods largely retrieve coarse-grained column-text associations, whereas integration systems assume supplied row-text links, schemas, or queries. LOKI instead transforms these implicit associations into explicit, interpretable join paths, organizes them into relation-consistent groups, and materializes them as typed integrated tables with sentence-level provenance. Comprehensive evaluations on real-world benchmarks demonstrate that LOKI consistently outperforms state-of-the-art multi-modal data discovery approaches, and materializes typed integrated tables with 0.982 macro typed-pair precision while being up to 40 times cheaper in LLM API cost than direct prompting.
