Knowledge Graphs
Data is fundamental to critical business decisions, as well as meeting reporting requirements. Organisations can be hindered in these areas via incosistent terminology, scattered data silos and poor interoperability.
Knowledge graphs are a powerful data storage medium that addresses these challenges head on. CMCL deploy semantic knowledge graphs to unify definitions, bridge silos and foster collaboration across departments, organisations and sectors.
Video of two terms being matched via a semantic layer
Knowledge graphs have proven to be powerful tools in the banking XXX and XXX industries, and are reaching maturity elsewhere. They are truly fundamental to our work with The World Avatar™. They offer a comprehensive way to represent the complex relationships that make up our world, and as such they underpin much of the ecosystem.
As such, CMCL have developed deep expertise applying knowledge graphs to a range of challenges in a wide variety of sectors.
Video of pipe and cable being matched as connection across sectors
Knowledge graphs are Consistent Unifying Flexible
Knowledge Graphs vs traditional databases
Flexibility
Data within knowledge graphs can be exported in a range of formats, from json to csv. In addition, due to their basis in ontologies, they are easily extensible via the introduction of new schemas.
Inference
For example, a knowledge graph could store the fact that CMCL is based in Cambridge, and separately, that Cambridge is in the UK. It can then infer that CMCL is based in the UK.
Scalability
For applications with many single interactions such as financial transactions, a traditional database would require many queries. Knowledge graphs can capture complex information flows with ease, scaling efficiently.
Derived Information Framework
The Derived Information Framework (DIF), developed as part of The World Avatar™ is a framework which CMCL uses to extend the capabilities of knowledge graphs. We leverage it to go beyond traditional knowledge graphs, seamlessly managing complex information interdependencies and provenance.
Knowledge graphs have rapidly gained popularity in both enterprise applications and research fields. They are highly useful for integrating diverse information sources and building common understanding. Dynamic knowledge graphs, with rapidly changing data, build upon this capability. However, there is a serious need for robust methods to deal with complex interdependencies and information provenance.
The framework handles these issues, combining knowledge graphs with software agents. It records provenance, tracking when and how pieces of information are obtained from others, instilling confidence in the validity of data and ensuring values are up-to-date. It allows for autonomous workflows which propagate changes/updates to data through the knowledge graph. Ripples through the triples!
Features
Live
Data is kept up to date, avoiding issues with mismatched caching in complex dynamic knowledge graphs.
Autonomous
Software agents autonomously cascade and integrate data, managed via an overarching architecture.
Tracked
Records of data provenance are automatically generated and marked up by agents via an ontology.
The Derived Information Framework has the power to solve complex challenges where information is rapidly changing and provenance is key. It has already been applied within the banking and chemicals industries and we are continuously discovering novel applications.
More information is available via The World Avatar™ website. Alternatively, get in touch with us directly.