From Natural Language to Control Signals: A Conceptual Framework for Semantic Channel Finding in Complex Experimental Infrastructure
🤖 AI Summary
Modern experimental facilities—such as particle accelerators and fusion devices—feature hundreds of thousands to millions of heterogeneous control and diagnostic channels. Their unstructured naming conventions, fragmented domain knowledge, and lack of semantic annotations severely impede precise mapping from natural language intents to specific signals, hindering monitoring, fault diagnosis, and AI-driven automation. To address this, we propose a four-paradigm synergistic framework: contextual dictionary lookup, constraint-aware hierarchical navigation, interactive agent-based exploration, and ontology-driven semantic search—first systematically decoupling signal semantics from facility-specific nomenclature. Integrating prompt engineering, hierarchical graph navigation, tool-augmented reasoning agents, and domain ontology-embedded retrieval, our approach is validated across four real-world facilities spanning orders of magnitude in scale. Expert-annotated query accuracy reaches 90–97%, significantly enhancing the reliability and scalability of large-facility interfaces powered by foundation models.
Application Category
📝 Abstract
Modern experimental platforms such as particle accelerators, fusion devices, telescopes, and industrial process control systems expose tens to hundreds of thousands of control and diagnostic channels accumulated over decades of evolution. Operators and AI systems rely on informal expert knowledge, inconsistent naming conventions, and fragmented documentation to locate signals for monitoring, troubleshooting, and automated control, creating a persistent bottleneck for reliability, scalability, and language-model-driven interfaces. We formalize semantic channel finding-mapping natural-language intent to concrete control-system signals-as a general problem in complex experimental infrastructure, and introduce a four-paradigm framework to guide architecture selection across facility-specific data regimes. The paradigms span (i) direct in-context lookup over curated channel dictionaries, (ii) constrained hierarchical navigation through structured trees, (iii) interactive agent exploration using iterative reasoning and tool-based database queries, and (iv) ontology-grounded semantic search that decouples channel meaning from facility-specific naming conventions. We demonstrate each paradigm through proof-of-concept implementations at four operational facilities spanning two orders of magnitude in scale-from compact free-electron lasers to large synchrotron light sources-and diverse control-system architectures, from clean hierarchies to legacy environments. These implementations achieve 90-97% accuracy on expert-curated operational queries.
Problem
Research questions and friction points this paper is trying to address.
Mapping natural-language intent to control-system signals
Overcoming inconsistent naming conventions in experimental platforms
Providing a framework for semantic channel finding across infrastructures
Innovation
Methods, ideas, or system contributions that make the work stand out.
Semantic channel finding framework for experimental infrastructure
Four paradigms mapping natural language to control signals
Proof-of-concept implementations achieve high accuracy across facilities
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