Ontotext GraphDB Named Innovator in Bloor’s Graph Database Market Research
SOFIA, Bulgaria, Feb. 11, 2019 /PRNewswire/ —
Leading independent technology research and analyst house Bloor Research has ranked Ontotext’s signature semantic graph database GraphDB as innovator in multi-model (hybrid) environments among dozens of graph databases.
In its Graph Database Market Update 2019, Bloor Research examines the latest market trends in vendor developments of graph databases.
A key finding of the 2019 market update shows that the knowledge graph has become an increasingly popular way to represent and find relationships out of vast amounts of data.
Apart from an overview of the latest developments, the authors of the report published briefs about vendors and used eight different metrics to score the capabilities of each vendor’s graph database: analytics, ease of use, features, integration, language, operations, performance and scalability.
Ontotext received the highest score among RDF databases in terms of operations capabilities and very high score for features, language and analytics.
Bloor noted that Ontotext GraphDB was one of the first vendors in the global graph database market. The graph technology that Ontotext uses performs much better than relational technology and databases when users want to understand relationships in large datasets.
A notable GraphDB feature is also the semantic similarity based on graph embedding. In GraphDB 8.7, Ontotext introduced a new plugin returning similar terms, documents and entities, which added support for concept-matching in knowledge graphs. The latest release, GraphDB 8.8, now allows users to perform semantic similarity searches based on the embedding of relationships in a graph (Subject-Predicate-Object triples or Predications) in a highly-scalable vector space model.
Ontotext is focused on text, content and related areas, and many of its clients are in some way involved in publishing or media. Yet, this does not mean that Ontotext’s GraphDB cannot be used in more general-purpose operational and hybrid operational/analytic use cases, Bloor added.
“Nevertheless, the company offers a one-stop shop for both the database and text mining, and the strength of this offering – the way that it works with enterprise knowledge graphs – is a significant differentiator for the company.”
Also mentioned in the paper is the Ontotext Platform, which extends GraphDB with text mining capabilities, interlinking text and graphs for enriching graphs with facts extracted from the text.
Ontotext helps enterprises to identify meaning across diverse datasets and massive amounts of unstructured information since 2000. Ontotext offers technology and services for the development of big knowledge graphs, interlinking multiple structured datasets.
Ontotext delivers products and content analytics solutions to enterprises like S&P, BBC, Financial Times, Elsevier, Wiley, UK Parliament, Kadastr.NL and Fujitsu.
In January 2019 Ontotext AD merged into Sirma AI JSC and became the core of Sirma Group’s Enterprise AI strategy. Sirma AI trades as Ontotext but extends its portfolio with computer vision and facial recognition technology.