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
At Center Stage V: Embedding Graphs in Enterprise Architectures via GraphQL, Federation and KafkaBlogFeaturedTechnology
Throwing Your Data Into the OceanBlogBusiness
Declarative Knowledge Graph APIsBlogTechnology
Ontotext Invents the Universe So You Don’t Need ToBlogTechnology
From Data Silos to Data Fabric with Knowledge GraphsBlogInformational
Star Wars: Knowledge Graph FederationFeaturedTechnology
RDF Levels the Advantages of Labeled Property Graphs & Keeps Three Key Benefits: Standards, Semantics & Interoperabilitywebinars
From Disparate Data to Visualized Knowledgewebinars
Knowledge Graph Maps: 20+ Application and 30+ Capabilitieswebinars
Knowledge Management Becomes Business Critical as Knowledge Graphs Improve Decision Making and Efficiencywebinars
Knowledge Graphs for Enterprise Data Managementwebinars
Reasoning with Big Knowledge Graphs: Choices, Pitfalls and Proven Recipeswebinars