MSLE: An ontology for Materials Science Laboratory Equipment. Large-Scale Devices for Materials Characterization

TL;DR

This paper presents MSLE, a comprehensive ontology for materials science laboratory equipment, integrating existing ontologies and using SKOS for hierarchical terminology, to improve equipment description and usage guidance.

Contribution

The paper introduces MSLE, a novel ontology combining SSN and MatVoc, with SKOS-based terminology organization, tailored for large-scale materials characterization devices.

Findings

Successfully integrated SSN and MatVoc into MSLE

Developed SKOS-based hierarchical equipment terminology

Validated constraints with a prototype using SHACL

Abstract

This paper introduces a new ontology for Materials Science Laboratory Equipment, termed MSLE. A fundamental issue with materials science laboratory (hereafter lab) equipment in the real world is that scientists work with various types of equipment with multiple specifications. For example, there are many electron microscopes with different parameters in chemical and physical labs. A critical development to unify the description is to build an equipment domain ontology as basic semantic knowledge and to guide the user to work with the equipment appropriately. Here, we propose to develop a consistent ontology for equipment, the MSLE ontology. In the MSLE, two main existing ontologies, the Semantic Sensor Network (SSN) and the Material Vocabulary (MatVoc), have been integrated into the MSLE core to build a coherent ontology. Since various acronyms and terms have been used for equipment, this paper proposes an approach to use a Simple Knowledge Organization System (SKOS) to represent the hierarchical structure of equipment terms. Equipment terms were collected in various languages and abbreviations and coded into the MSLE using the SKOS model. The ontology development was conducted in close collaboration with domain experts and focused on the large-scale devices for materials characterization available in our research group. Competency questions are expected to be addressed through the MSLE ontology. Constraints are modeled in the Shapes Query Language (SHACL); a prototype is shown and validated to show the value of the modeling constraints.