统一跨异构知识源的检索，让自然语言查询自动匹配最佳数据源。
OmniRetrieval: Unified Retrieval across Heterogeneous Knowledge Sources
- 用自然语言查询自动识别适配的知识源并调用其原生接口。
- 在13个数据集、309个知识库上超越单一源基线模型。
- 保留各数据源结构优势，适合多源知识融合场景。
现实中的信息需求涉及结构各异的知识源，包括非结构化文本、关系表、知识图谱和属性图等。现有检索系统仅支持单一源且使用固定查询语言，导致不同知识源间界面不兼容，整体信息碎片化。直接将所有源映射到统一空间会丧失各源的结构特性（如模式、本体、组合操作符），削弱表达能力。因此，高效跨源检索不应追求同质化，而需构建一个能与各源对话的统一层。为此，我们提出OmniRetrieval框架：接收自然语言查询，自动识别适配的知识源，并向其原生执行引擎发送原生查询。在涵盖13个数据集、309个独立知识库的广泛基准测试中，OmniRetrieval超越单源基线模型，证明其可作为异构知识源的通用接口，同时保持各源的结构性价值。
原文摘要 · Abstract (English)
Real-world information needs require access to structurally diverse knowledge sources, from unstructured text and relational tables to knowledge graphs and property graphs. Existing retrievers, however, operate over one source at a time under a fixed query language, leaving the broader landscape of available knowledge fragmented behind incompatible interfaces. A natural attempt at unification would collapse these sources into a shared space, but this erases the structural affordances (such as schemas, ontologies, compositional operators) that give each source its expressive power. Effective retrieval over diverse knowledge, therefore, requires not homogenization but an overarching layer that meets each source on its own terms. To achieve this, we present OmniRetrieval, a framework that takes any natural-language query, identifies appropriate knowledge sources, and dispatches source-native queries to their native execution engines. Across an extensive benchmark spanning 13 datasets and 309 distinct knowledge bases over text, relational, and graph-structured sources, OmniRetrieval exceeds single-source baselines, demonstrating that it can serve as a general-purpose interface to the heterogeneous sources while preserving the structural distinctions that make each source valuable.
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