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 it has recently reviewed.
In its Graph Database Market Update 2019, Bloor Research examines the latest market trends in vendor developments of graph databases since its previous update on the subject two years ago.
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 – Research Director Philip Howard and Senior Researcher Daniel Howard – published briefs about vendors and used eight different metrics to score the capabilities of each vendor’s graph database.
The eight metrics (listed alphabetically) are: 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.
Reviewing GraphDB and Ontotext’s role in the global graph database market, Bloor Research observed that Ontotext was one of the first vendors.
In addition to commercial clients, Ontotext has won EU funding, which has enabled it to work on more than 30 different projects focused on the Semantic Web, Linked Data and Open Data, Bloor Research said.
The graph technology that Ontotext uses performs much better than relational technology and databases when users want to understand relationships in large datasets, Bloor noted.
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.
For more information, contact Doug Kimball, Chief Marketing Officer at Ontotext