Ontotext Platform Download

To Try Ontotext Platform:

  1. Before downloading and installing all components packaged as Docker images, run your Docker Composer. (If you need guidance, please use these instructions.)
  2. Fill out the form to receive a valid license key from one of our representatives.
  3. Run Ontotext Semantic Object Service.

Fill out the form to give Ontotext Platform a try!

Request Ontotext Platform

Who is Ontotext Platform For?


Business Analysts (BA)

BAs specify the requirements and can define semantic business objects, which serve as GraphQL interface and contract between the knowledge graph and its users, while hiding the low-level implementation aspects.

Subject Matter Experts & Knowledge Engineers

SMEs & Knowledge Engineers define the semantic schemas of the knowledge graph by selecting and combining taxonomies and vocabularies, reusing existing ontologies and creating new ones. They are often involved in the refinement and incremental development of these resources.

Architects & Data Engineers

Architects & Data Engineers identify relevant data sources and develop cleaning, mapping and linking procedures. They also determine the data schema of the knowledge graph (KG) and are in charge of the automation of its bootstrapping and updates. Often architects have a leading role in the technical development of the KG and determine how the graph will augmented via inference and analytics.

Solution Architects & Application Developers

Solution Architects & Application Developers access the graph via GraphQL, SPARQL or other platform APIs to build end-user applications. They are also involved in integration of knowledge graph services with the functionality of existing systems.

DataOps and MLOps

DataOps and MLOps manage the deployment, the updates and the daily operations (e.g. monitoring of availability, performance, data quality, access control) of the platform, including the different engines used in specific storage and search architecture.

Data Scientists

Data Scientists can be involved to configure and tune the numerous machine learning models and analytics services used in the platform for data linking, disambiguation, classification, recommendation semantic search and other activities. In many cases, data scientists consume the unified data in the knowledge graph for downstream analytics (e.g. BI).