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Semantic Information Management in Low-Temperature Plasma Science and Technology with VIVO

Ihda Chaerony Siffa, Robert Wagner, Markus M. Becker

TL;DR

The paper addresses the need for machine-readable, interoperable semantic management of low-temperature plasma (LTP) research data by proposing Plasma-O, a domain-specific ontology, and its deployment within the VIVO platform to form the Plasma-KG. Plasma-O extends Plasma-MDS and is formulated within the description logic $ALC$ to enable automated reasoning over LTP concepts such as Plasma Study and Plasma Experiment, with rich, inverse relationships among plasma sources, media, targets, devices, and outputs. Integrated in VIVO (v1.15), Plasma-O supports community-driven data ingestion and linking, enabling semantic cataloging of plasma sources and datasets and SPARQL-based information retrieval, demonstrated through competency questions. The work provides publicly accessible ontology and Plasma-KG resources (Plasma-O v0.7.0 with 61 classes, 105 object properties, 12 individuals) and envisions expanding the ontology and integrating with RDM workflows, potentially benefiting cross-domain interoperability and FAIR data practices in LTP and related fields. Overall, the framework offers a reusable, scalable approach to semantic information management that can be extended to other domains and supports richer, machine-readable discovery of research information.

Abstract

Digital research data management is increasingly integrated across universities and research institutions, addressing the handling of research data throughout its lifecycle according to the FAIR data principles (Findable, Accessible, Interoperable, Reusable). Recent emphasis on the semantic and interlinking aspects of research data, e.g., by using ontologies and knowledge graphs further enhances findability and reusability. This work presents a framework for creating and maintaining a knowledge graph specifically for low-temperature plasma (LTP) science and technology. The framework leverages a domain-specific ontology called Plasma-O, along with the VIVO software as a platform for semantic information management in LTP research. While some research fields are already prepared to use ontologies and knowledge graphs for information management, their application in LTP research is nascent. This work aims to bridge this gap by providing a framework that not only improves research data management but also fosters community participation in building the domain-specific ontology and knowledge graph based on the published materials. The results may also support other research fields in the practical use of knowledge graphs for semantic information management.

Semantic Information Management in Low-Temperature Plasma Science and Technology with VIVO

TL;DR

The paper addresses the need for machine-readable, interoperable semantic management of low-temperature plasma (LTP) research data by proposing Plasma-O, a domain-specific ontology, and its deployment within the VIVO platform to form the Plasma-KG. Plasma-O extends Plasma-MDS and is formulated within the description logic to enable automated reasoning over LTP concepts such as Plasma Study and Plasma Experiment, with rich, inverse relationships among plasma sources, media, targets, devices, and outputs. Integrated in VIVO (v1.15), Plasma-O supports community-driven data ingestion and linking, enabling semantic cataloging of plasma sources and datasets and SPARQL-based information retrieval, demonstrated through competency questions. The work provides publicly accessible ontology and Plasma-KG resources (Plasma-O v0.7.0 with 61 classes, 105 object properties, 12 individuals) and envisions expanding the ontology and integrating with RDM workflows, potentially benefiting cross-domain interoperability and FAIR data practices in LTP and related fields. Overall, the framework offers a reusable, scalable approach to semantic information management that can be extended to other domains and supports richer, machine-readable discovery of research information.

Abstract

Digital research data management is increasingly integrated across universities and research institutions, addressing the handling of research data throughout its lifecycle according to the FAIR data principles (Findable, Accessible, Interoperable, Reusable). Recent emphasis on the semantic and interlinking aspects of research data, e.g., by using ontologies and knowledge graphs further enhances findability and reusability. This work presents a framework for creating and maintaining a knowledge graph specifically for low-temperature plasma (LTP) science and technology. The framework leverages a domain-specific ontology called Plasma-O, along with the VIVO software as a platform for semantic information management in LTP research. While some research fields are already prepared to use ontologies and knowledge graphs for information management, their application in LTP research is nascent. This work aims to bridge this gap by providing a framework that not only improves research data management but also fosters community participation in building the domain-specific ontology and knowledge graph based on the published materials. The results may also support other research fields in the practical use of knowledge graphs for semantic information management.
Paper Structure (8 sections, 2 equations, 4 figures, 3 tables)

This paper contains 8 sections, 2 equations, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Methods
  3. Ontology for Low-Temperature Plasma Science and Technology
  4. LTP Knowledge Graph Development with VIVO
  5. Results
  6. Semantic Cataloging and Browsing of Domain-Specific Information and Data
  7. Semantic Information Retrieval Using SPARQL
  8. Conclusion and Outlook

Figures (4)

  • Figure 1: (a) Typical experiment setup found in low-temperature plasma research. (b) Schematic representation of the relationships between the components within the experiment.
  • Figure 2: Simplified excerpt of Plasma-O illustrating selected classes and their relationships with each other and classes from external ontologies. Inverse properties are not visualized for simplicity.
  • Figure 3: Example workflow for creating an instance of Plasma Source.
  • Figure 4: Example of a device catalog within the plasma knowledge graph showcasing plasma sources and diagnostic devices. Faceted browsing feature enhances navigation across the semantic information landscape of the LTP domain.