Ontotext Platform

Organize your information and documents into enterprise knowledge graphs

Ontotext Platform makes data management and analytics work in synergy:

  • Connect and publish complex enterprise knowledge with standard-compliant semantic graph database;
  • Customize and apply analytics to link documents to graphs, extract new facts, classify and recommend content;
  • Access data via GraphQL to ease application development.
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Why Choose Ontotext Platform?

Agile enterprise data management

  • Connect data into reusable knowledge graphs;
  • Accumulate data preparation efforts by describing and linking data to make it easy to find and use for further analytics;
  • Apply better knowledge governance and quality using graph and semantic technology stack.

Native semantic model

  • Support ontologies, reasoning and semantic integration;
  • Preserve the information metadata, source and provenance;
  • Put all data into the right context to enable deep data and analytics.

Architecture connecting data producers and consumers

  • Use an architecture based on open standards for connecting information architects with software developers;
  • Expose GraphQL access to semantic models;
  • Preserve the full complexity of the ontology models.

What is Ontotext Platform?


Ontotext Platform consists of a set of databases, machine learning algorithms, APIs and tools we use to build various solutions for specific enterprise needs.

What can you do with Ontotext Platform?

  • Develop and maintain knowledge graphs from diverse data. Continuously integrate, normalize and interlink data from diverse sources, and maintain data quality upon updates.
  • Automatically generate GraphQL access from ontologies. Declare simplified information views to ease data consumption and implement access control.
  • Generate semantic metadata and extract knowledge. Use text analysis to extract knowledge from unstructured documents and generate semantic metadata.
  • Efficiently generate SPARQL queries. No need to write and optimize complex queries.
  • Easily integrate applications, including non-semantic sources. Federation, schema stitching and data virtualization.
  • Adopt developer friendly tooling. Implement user interfaces directly from the shape of data, minimizing the information payload by using a rich ecosystem of developer tools.
  • Use authorization and authentication. Apply a generic model for controlling information access.
  • Make use of high-availability, query and search via GraphDB. Employ the most robust database engine for knowledge graphs, featuring reasoning, semantic similarity and ranking.
  • Scale data, query and transaction loads via integration with ElasticSeach and MongoDB.
  • Run a cloud-agnostic deployment with Kubernetes. Spin up development and production environments in minutes.

Ontotext Platform Architecture

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