arXiv:2605.29250v1 Announce Type: cross Abstract: 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 g
The proliferation of diverse data types and the increasing complexity of real-world information needs are driving demand for unified retrieval solutions.
This research addresses fundamental limitations in current AI retrieval systems, promising more comprehensive and accurate information access across heterogeneous data sources, essential for advanced AI applications.
Retrieval systems may evolve from siloed, source-specific approaches to integrated platforms capable of understanding and querying across text, tables, and knowledge graphs simultaneously.
- · AI platform developers
- · Enterprises with diverse data
- · Researchers
- · Data integration companies
- · Monolithic, single-source retrieval systems
- · Specialized, narrow data querying tools
Improved performance and accuracy for AI models requiring diverse knowledge.
Reduced complexity and faster development cycles for AI applications that draw from multiple data types.
New paradigms for knowledge representation and interaction, potentially leading to more human-like AI reasoning.
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Read at arXiv cs.LG