The zbMATH Open Knowledge Graph: Tracing Centuries of Mathematical Research
The zbMATH Open Knowledge Graph is a large-scale RDF knowledge graph covering more than 250 years of mathematical scholarship. It integrates expert-curated semantic content including reviews, keywords, subject classifications, software references, and disambiguated authorship. The graph comprises 34 million entities and 168 million RDF triples represented using established Semantic Web vocabularies, supporting interoperability and FAIR data principles.
The zbMATH Open Knowledge Graph is a large-scale RDF knowledge graph covering more than 250 years of mathematical scholarship. Unlike existing scholarly knowledge graphs that primarily capture bibliographic metadata and citation structures, it integrates expert-curated semantic content, including reviews, keywords, subject classifications, software references, and disambiguated authorship. The graph comprises 34 million entities and 168 million RDF triples represented using established Semantic Web vocabularies, supporting interoperability and FAIR data principles. The paper demonstrates query-driven historically grounded scholarly exploration use cases, illustrating how the knowledge graph can surface relationships and patterns that may be difficult to identify from bibliographic and citation information alone.
The knowledge graph uses RDF and established Semantic Web vocabularies to represent mathematical knowledge, enabling fine-grained exploration of concepts, research fields, and scholarly relationships over time. Its scale (34 million entities, 168 million triples) and integration of expert-curated semantic content distinguish it from citation-only scholarly graphs.
The zbMATH Open KG provides an open semantic infrastructure for mathematical research, potentially enabling new tools for literature discovery, research trend analysis, and scholarly impact assessment. Its FAIR data principles and interoperability may encourage adoption by academic institutions and digital libraries.
The knowledge graph could support commercial or institutional services for mathematical literature search, research analytics, and recommendation systems. Its open nature may lower barriers for startups and academic projects to build on the data.
Observable next signals include publication of follow-up papers demonstrating additional use cases, adoption of the knowledge graph by third-party research tools, and integration with other scholarly knowledge graphs or bibliographic databases.