Ontotext GraphDB Passes the Linked Data Benchmarking Council's Social Network and the Semantic Publishing Benchmarks
Benchmarks determine the Ontotext GraphDB is the only RDF engine proven to deal efficiently with both graph analytics and metadata management workloads
NEW YORK, April 12, 2023 /PRNewswire/ -- Ontotext, the leading global provider of enterprise knowledge graph (EKG) technology and semantic database engines, today announced that Ontotext GraphDBтм is the first engine to pass both Linked Data Benchmarking Council's (LDBC) Social Network Benchmark (SNB) and Semantic Publishing Benchmarks (SPB), proving its unique capability to handle graph analytics and metadata management workloads simultaneously. The results from the LDBC - a non-profit organization defining standard graph benchmarks to foster a community around graph processing technologies - found Ontotext GraphDB to be the most versatile Graph Database Engine allowing data from different sources to be interlinked, contextualized, and normalized in a graph that allows for consistent and unambiguous interpretation.
As a proven Resource Description Framework (RDF) engine for knowledge graph, Ontotext GraphDB enables organizations to link diverse data, index it for semantic search, and enrich it via text analysis to build large scale knowledge graphs. This recognition is significant, as the benchmark indicates a clear separation between Labeled Property Graph (LPG) engines and RDF engines. Historically, LPG engines were optimized to deal with graph analytics and RDF engines were designed for data publishing and metadata management. In the benchmark, RDF engines were audited only on SPB, while LPG and other graph analytics-optimized designs were audited only on SNB. The benchmark simulated analytical queries against social networks data such as messages, comments, people related to other people, cities, universities, companies, etc. and affirmed that Ontotext GraphDB passed SNB's Interactive Workload at scale factor 30 (SF30) across a graph of 1.5 billion edges.
Enterprise knowledge graphs require graph databases that facilitate advanced data integration and metadata data management scenarios where an EKG can be used for data fabrics or serve as a data hub between diverse data and content management systems. The same engines are expected to efficiently deal with computationally challenging data analytics, discovering multi-hop relationships across networks of concepts, entities, assets, documents, and other resources.
"Our mission is to offer our users an enterprise-ready database delivering stable performance across different graph use cases. Passing external benchmark audits with a generally available version reflects our transparent engineering culture of being an advisor to our clients," said Vassil Momtchev, CTO, at Ontotext. "In both benchmarks, the engine scales with the complex read and write operations load while preserving its ACID compliance and graph consistency."
About Ontotext
As the leading global provider of enterprise knowledge graph technology and semantic database engines, Ontotext helps enterprises to identify meaning and connections across diverse datasets and massive amounts of unstructured information. Ontotext's technology and services deliver value through semantic knowledge graphs, linking multiple structured and unstructured datasets to help customers achieve enhanced decision making, support knowledge growth and acquisition, deliver insights discovery, and ensure AI is properly educated. The company's knowledge graph technology helps businesses to connect data and define relationships to get the most out of business-critical data. The Ontotext GraphDBтм engine and Ontotext Platform are credited for powering business-critical systems in some of the largest financial services and life sciences organizations, media, market intelligence agencies, car and aerospace manufacturers. To learn more visit or follow them on LinkedIn or Twitter.
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