Semantic Objects
Transform your company’s information and documents into enterprise knowledge graphs and uncover complex relationships in your knowledge using intuitive GraphQL, streamlining insights without requiring database expertise.
Semantic objects are now part of the Graphwise GraphDB component.
Synergize data management and analytics
Discover complex data relationships through user-friendly GraphQL, reducing the time and technical expertise required for data analysis.
Easy access interconnected enterprise data, improving information availability for applications and reducing dependency on specialized database knowledge.
Integrate scalable knowledge graphs into business applications for rapid adaptation to evolving data needs and application requirements.
Why Semantic Objects?
Agile enterprise data management
- Connect data into reusable knowledge graphs.
- Link data for enhanced analytics.
- Ensure quality governance with graph and semantic technology.
Native semantic model
- Support ontologies, reasoning, and integration.
- Preserve vital metadata, sources and provenance.
- Contextualize data for deeper insights.
Connecting data producers and consumers
- Utilize an open standards architecture to connect information architects with software developers.
- Expose GraphQL access to semantic models.
- Maintain ontology model complexity.
Developer-friendly tools
- Build user interfaces directly from the shape of data, minimizing the information payload.
- Scale data, query and transaction loads with ElasticSearch and MongoDB integration.
- Run a cloud-agnostic deployment and deploy rapidly with Kubernetes for cloud flexibility.
Industry leaders trust us
FAQs
Have questions? We’re here to help.
Question text goes here
No, this is a self-paced learning path experience with free access. You will need to register for the learning system.
Question text goes here
There are a couple of advantages to the Academy compared to the freely available Ontotext documentation and webinars. These training paths are designed with a specific audience profile in mind:
- Knowledge Graph Engineers, who are designing the data models with RDFS and maybe OWL, extending standard data models if necessary, developing SHACL shapes to ensure data quality, and identifying which data sets should be combined to create knowledge graphs for their enterprise’s applications.
- Application Developers who are using their JavaScript framework, or their preferred programming language, to efficiently query, add data to, and delete data from knowledge graphs stored in GraphDB on the backend of their system. They are not interested in rich ontology modeling, but they want to know about the various APIs that GraphDB supports and which are best for what.
- DevOps staff, who want to know how to configure a set of clusters to support the applications created by the developers, especially if those applications are being used by thousands of users.
Question text goes here
This training is meant to provide data scientists, researchers, product managers and developers with the theoretical and practical knowledge necessary to design a small proof-of-concept project that demonstrates the utility of Semantic Technology and a graph database.
Although you will need to have some basic understanding of Semantic Technology and programming and query languages, no detailed knowledge is required. All advanced concepts exposed in the training will be explained in sufficient detail.
Question text goes here
Each learning path consists of three or four courses, and each course typically takes a half a day or a little more.
Related posts
From Disparate Data to Visualized Knowledge Part III: The Outsider Perspective
Read our final post from this series focusing on how GraphDB and Ontotext Platform provide an architecture that can work on any infrastructure resulting in a well-deployed and well-visualized knowledge graph.
Throwing Your Data Into the Ocean
Read about how knowledge graphs help data preparation for analysis tasks and enables contextual awareness and smart search of data by virtue of formal semantics.
Declarative Knowledge Graph APIs
Stop wasting time, manually building data access code. Let the Ontotext platform auto-generate a fast, flexible, and scalable GraphQL API over your RDF knowledge graph.
Need further assistance?
If you can’t find the answer you’re looking for, our dedicated team is here to help.