What Are Knowledge Graphs?
A Knowledge Graph turns Data into Knowledge
Highly Expressive Graphs Used to Connect and Enrich All Data
Represent a knowledge domain
Represent facts (data) and models (metadata) in the same way
Are based on open standards, from top to bottom
Are highly expressive graphs used to connect and enrich all data
- A network of nodes and links, not tables of rows and columns
Knowledge graphs integrate data facts with a semantic layer, providing a rich, machine-understandable representation of the meaning of data.
See Knowledge Graphs in Action
Learn about the strengths and limitations of Knowledge Graphs versus Property Graphs and their respective capabilities.
Learn about key knowledge graph characteristics that meaningfully bridge enterprise metadata silos.
Learn about data fabric and the backbone of its design an Enterprise Knowledge Graph capturing information about data sources.
Explore why connecting the various models of data sources is important in data governance.
A Powerful Platform for Enterprise Data Integration
Knowledge graphs are an ideal foundation for bridging and connecting enterprise metadata silos. Every resource in a TopBraid knowledge graph has a globally unique dereferenced web identifier – a URI. Thus, can be reliably referred to and accessed from any application.
Graphs are the most flexible formal data structures (making it simple to map other data formats to graphs) that capture explicit relationships between items so that you can easily connect new data items as they are added and traverse the links to understand the connections.
Knowledge graphs are able to accommodate diverse data and metadata that adjusts and grows over time, much like living things do.
The semantics of data are explicit and include formalisms for supporting inferencing and data validation. As a self-descriptive data model, knowledge graphs enable data validation and can offer recommendations for how data may need to be adjusted to meet data model requirements.
The meaning of the data is stored alongside the data in the graph, in the form of the ontologies or semantic models. This makes knowledge graphs self-descriptive.
How to Get Started with Knowledge Graphs?
Implementing effective Data Governance can be challenging. TopBraid EDG provides a solution by readily converting existing information into a knowledge graph.
Data Governance Challenges
- Galaxies of data
- Diversity of perspectives – Business, Technical, Regulatory
- Diversity of representation
- Complex enterprise landscape
- Auto-create knowledge graphs representing data sources
- Link to other relevant enterprise information e.g., systems, policies, infrastructure, activities
- Enrich, discover, connect
- Use to guide business decisions
More Details about Knowledge Graph Technologies
See how knowledge graphs guide and focus ML and serve as a unifying fabric for different AI algorithms.
Want to take advantage of external knowledge graphs by connecting to them? Learn how to do this.
Learn how to apply knowledge graph technologies and key challenges faced by users.
Learn how knowledge graphs bridge metadata silos in a flexible, evolvable way.