Ontotext Releases Version 3.0 of its Graphing Platform

Ontotext is updating its signature platform with new GraphQL interfaces to make it easier for application developers to access knowledge graphs without tedious development of back-end APIs or complex SPARQL. The underlying Semantic Object service implements an efficient GraphQL to SPARQL translation optimized for GraphDB, as well as a generic configurable security model.

Knowledge graphs increase the diversity and the depth of the information available for business intelligence, fostering deep insights and better decision making.

A key challenge knowledge graphs overcome is combining the traditional enterprise data management systems for data quality, governance and master data, with modern analytics and machine learning algorithms.

The result leads to more adaptive and intelligent knowledge management solutions efficiently operating on top of the existing data silos applications.

Ontotext Platform 3.0 features improvements to enable simpler and faster graph navigation. The new GraphQL user-centric API exposes an additional interface to start consuming complex information much faster at a lower cost.

Business analysts (BAs) and subject matter experts (SMEs) are in charge to define Semantic Objects as specific views, abstracting developers from the complexity and peculiarities of the knowledge graph.

Based on these definitions, the Semantic Object service implements an efficient GraphQL to SPARQL translation optimized for GraphDB – Ontotext’s semantic graph database engine. This service implements a generic configurable security model, enabling access control without back-end development.

“The Ontotext Platform and knowledge graphs help you organize all your enterprise metadata, reference and master data,” said Vassil Momtchev, CTO, Ontotext.” You can efficiently maintain it up-to-date with the existing silos or third party data providers, as well as lower the implementation costs by delivering ready-to-use patterns. The Platform and its GraphQL API query language help you start implementing the application directly on top of the ontology model.”

The Platform is cloud-agnostic and supports an easy extension with custom services packaged as Docker containers. It includes operational dashboards for service monitoring, efficient metric collection services, alerting, as well as all other functionalities necessary for delivering high-availability business-critical production services.

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