OmniRetrieval:跨异构知识源的统一检索
阅读原文· arxiv.org现实世界的信息需求需要访问结构多样的知识源,但现有检索器一次只能处理单一类型源。OmniRetrieval 框架能接收自然语言查询,识别合适知识源,并向各源的本地执行引擎发送原生查询。在涵盖文本、关系型和图结构知识源的13个数据集与309个知识库的广泛基准测试中,OmniRetrieval 的性能超越了单源基线,证明其可以作为异构知识源的通用接口,同时保留了每个源独特的结构优势。
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.