๐ค AI Summary
Low-temperature plasma (LTP) research has long suffered from semantic gaps in data management, inadequate implementation of FAIR principles, and barriers to cross-institutional data sharing. To address these challenges, this work introduces the first domain-specific knowledge graph framework for LTP: it leverages the novel ontology Plasma-O and integrates the VIVO semantic platform to enable RDF/OWL modeling and semantic integration of heterogeneous dataโincluding scholarly publications, experimental parameters, and instrumentation metadata. The framework supports community-driven ontology evolution and collaborative knowledge curation, thereby substantially enhancing data discoverability, interoperability, and reusability. As the first scalable, open knowledge graph infrastructure for LTP, it establishes a foundational paradigm for standardized scientific information management and intelligent retrieval in the domain.
๐ 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.