Neuro-Relational Programs: A Unified Framework for Queries and Neural Computation on Structured Data
Researchers introduced Neuro-Relational Programs (NRPs), a declarative query language that combines relational database queries with neural computation by extending Datalog-style rules to process embeddings alongside relational data. NRPs unify existing approaches like Graph Neural Networks and Deep Homomorphism Networks within a single formal framework. This work establishes a theoretical foundation for neural-symbolic computation, with implications for how machine learning systems can reason over structured data.
Neuro-Relational Programs represent a formal approach to integrating neural computation with relational database queries. Rather than converting databases into graphs for neural processing, NRPs operate directly on relational data while associating tuples with numeric vector embeddings. The framework extends Datalog-style declarative rules with operations for combining, aggregating, and transforming embeddings, allowing relational reasoning and learnable components to work together. The authors demonstrate that natural syntactic fragments of NRPs recover existing neural architectures and query formalisms, including Graph Neural Networks and Deep Homomorphism Networks. They characterize the expressive power of unrestricted NRPs through connections to first-order logic with counting (FOCQ) and uniform TC⁰ complexity classes, establishing a precise theoretical foundation for the framework.
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- arXiv cs.LGCenter
Neuro-Relational Programs: Unifying Queries and Neural Computation over Structured Data
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