TopBraid Explorer™ is a scalable and easy to use interface for working with knowledge graphs. TopBraid EDG users can publish asset information for use in Explorer. TopBraid Explorer lets people and applications search, query and view all published information.
TopBraid Explorer today uses the same technical platform as TopBraid EDG. Going forward, many capabilities will remain shared. At the same time, TopBraid Explorer will begin leveraging Graphzelle™ – the first graph database with built-in integrated support for W3C Standards (for schema definition, data validation, inferencing and query) and GraphQL (for query and APIs).
While TopBraid EDG will continue to be optimized for information curation and workflows, TopBraid Explorer, using Graphzelle, will be more optimized for delivering best query performance over combined information for the large numbers of users.
As organizations start treating data as an enterprise asset and investing in data related projects, it is inevitable they have to know the meaning and lineage of their data in order to make well informed business decisions, operate more effectively, find new sources of revenue and meet regulations.
TopBraid EDG implements data governance processes needed by organizations to meet the goals of:
- providing visibility for all important data irrespective of source and format
- enabling traceability of data as it flows across different systems in support of business activities
- making the meaning of data explicit and well understood
The combined output of the data and metadata curation is needed by data scientists, data analysts, business analysts and managers, IT staff – in short, any and all data consumers and producers. Published to TopBraid Explorer, information collected by TopBraid EDG (one or multiple EDG nodes and environments) can be used by all data stakeholders. TopBraid Explorer offers interactive user interfaces for accessing enterprise knowledge graphs.
You may have heard the term “knowledge graph” from Google or Microsoft. These companies use knowledge graphs for smart search. Knowledge graphs provide an equally powerful platform for enterprise data integration and reuse.
Here are some characteristics of knowledge graphs. They are:
- Graphs – 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, equally easily, traverse the links to understand the connections.
- Evolvable – able to accommodate diverse data and metadata that adjusts and grows over time, much like living things do.
- Semantic – 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, a single place to find the data and understand what it’s about.
- Intelligent – semantics of data is explicit and includes formalisms for supporting inferencing and data validation. The self-descriptive data model enables data validation and can offer recommendations for how data may need to be adjusted to meet the data model requirements. It also enables drawing conclusions and new information from the available data.