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.
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
Resource Center
-
RDF Levels the Advantages of Labeled Property Graphs & Keeps Three Key Benefits: Standards, Semantics & Interoperability
webinars -
From Disparate Data to Visualized Knowledge
webinars -
Knowledge Graph Maps: 20+ Application and 30+ Capabilities
webinars -
Knowledge Management Becomes Business Critical as Knowledge Graphs Improve Decision Making and Efficiency
webinars -
Knowledge Graphs for Enterprise Data Management
webinars -
Reasoning with Big Knowledge Graphs: Choices, Pitfalls and Proven Recipes
webinars