arXiv:2606.11946v1 Announce Type: cross Abstract: The conventional approach to deep learning over relational databases applies neural models, such as Graph Neural Networks (GNNs), to a graph representation of the database. Recent approaches instead operate on databases directly, associating tuples with embeddings and extending query mechanisms to jointly process embeddings and relational content. Inspired by these developments, we introduce Neuro-Relational Programs (NRPs), a declarative query language for relational databases whose facts carry numeric vector embeddings. NRPs extend Datalog-st
The increasing complexity of data and the maturity of deep learning models are driving innovations that bridge traditional database paradigms with neural computation.
This development represents a significant step towards more sophisticated AI agents capable of natively learning and reasoning over structured relational data without explicit graph conversions.
AI models can now directly embed and process relational database content, potentially leading to more efficient and powerful ways of querying and learning from structured information.
- · AI researchers and developers
- · Database technology providers
- · Enterprises with large relational datasets
- · AI agents developers
- · Traditional graph database conversion tools
- · Purely symbolic AI systems
AI models will gain direct, enhanced capabilities for learning and inferring from structured data.
This could lead to a new class of more intelligent and adaptable AI applications that directly interact with enterprise data stores.
The integration of neural and relational processing might redefine how data is stored, queried, and utilized across industries, fostering a new era of 'data-native AI'.
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Read at arXiv cs.LG