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A Core Ontology for Particle Accelerators: Interoperable Data and Workflows Across Facilities

Chris Tennant

TL;DR

This paper argues that accelerator data are fragmented by facility-specific conventions and introduces a compact core ontology built on RDF/OWL to provide a shared semantic backbone. Each facility supplies a profile and data slices that map local devices and channels to the core concepts, enabling cross-facility queries via SPARQL without renaming local channels. The approach complements existing middle layers and demonstrates a cross-facility demonstration between two beamlines, highlighting semantic interoperability and reusable tooling. The work envisions broader benefits for FAIR data, automated workflows, and governance to sustain cross-site collaboration, with a practical path for incremental adoption.

Abstract

We propose a small, shared core ontology for particle accelerators that provides a semantic backbone for interoperable data and workflows across facilities. The ontology names key device types, signals, parameters, and regions, and relates them through explicit properties (e.g., hasSetpoint, hasReadback, partOf). Each site contributes a lightweight facility bundle, a profile that maps local conventions into the shared vocabulary plus data slices that instantiate those mappings, without renaming channel addresses or changing existing systems. Using standard W3C technologies, the approach supports both sparse and rich descriptions. We demonstrate the idea on two beamline segments at different laboratories. A single semantic query is expressed once and evaluated against both knowledge bases, returning the locally correct PVs. The ontology thereby enables not only portable workflows but also interoperable data, since measurements and catalogs are annotated with shared semantics rather than facility-specific names. The framework complements, rather than replaces, existing middle layers and lattice/data standards, and it creates a stable foundation for reusable tools and agentic workflows.

A Core Ontology for Particle Accelerators: Interoperable Data and Workflows Across Facilities

TL;DR

This paper argues that accelerator data are fragmented by facility-specific conventions and introduces a compact core ontology built on RDF/OWL to provide a shared semantic backbone. Each facility supplies a profile and data slices that map local devices and channels to the core concepts, enabling cross-facility queries via SPARQL without renaming local channels. The approach complements existing middle layers and demonstrates a cross-facility demonstration between two beamlines, highlighting semantic interoperability and reusable tooling. The work envisions broader benefits for FAIR data, automated workflows, and governance to sustain cross-site collaboration, with a practical path for incremental adoption.

Abstract

We propose a small, shared core ontology for particle accelerators that provides a semantic backbone for interoperable data and workflows across facilities. The ontology names key device types, signals, parameters, and regions, and relates them through explicit properties (e.g., hasSetpoint, hasReadback, partOf). Each site contributes a lightweight facility bundle, a profile that maps local conventions into the shared vocabulary plus data slices that instantiate those mappings, without renaming channel addresses or changing existing systems. Using standard W3C technologies, the approach supports both sparse and rich descriptions. We demonstrate the idea on two beamline segments at different laboratories. A single semantic query is expressed once and evaluated against both knowledge bases, returning the locally correct PVs. The ontology thereby enables not only portable workflows but also interoperable data, since measurements and catalogs are annotated with shared semantics rather than facility-specific names. The framework complements, rather than replaces, existing middle layers and lattice/data standards, and it creates a stable foundation for reusable tools and agentic workflows.

Paper Structure

This paper contains 12 sections, 3 figures.

Table of Contents

  1. Introduction
  2. Key Definitions
  3. Motivation
  4. Interoperability Challenges in Data and Workflows
  5. A Core Accelerator Ontology
  6. A Facility Bundle
  7. Facility Profiles
  8. Facility Data Slices
  9. Relationship to Traditional Middle Layers
  10. Demonstration: A Semantic Bridge Between Two Beamlines
  11. Broader Implications and Opportunities
  12. Summary

Figures (3)

  • Figure 1: Hierarchical class structure showing subClassOf relationships (denoted by arrows) in an accelerator core ontology.
  • Figure 2: Schematic view of a shared core accelerator ontology and facility bundles. Each facility reuses the same core TBox, contributes its own profile (mapping local conventions into the shared vocabulary), and populates facility-specific data slices (ABox instances) derived from existing local systems. The core ontology is reused verbatim, only the profile and data differ.
  • Figure 3: Example of an LLM transforming a natural language question into a formal SPARQL query. Despite the minor syntax differences, the two queries are functionally equivalent. When applied to each materialized graph, the correct PVs are returned for each facility.