In the last blog posts [1, 2] in this series by Fredric Landqvist and Peter Voisey we have outlined for you, at a high level, about the benefits of making data smarter and F.A.I.R.,ideally made findable through a shareable, but controlled, type of Information Commons. In this post, we introduce you to Knowledge Graphs (based on Semantic Web Technologies), the source for the magic of smart and FAIR data automation. Data that is findable, accessible, interoperable and reusable. They can help tackle a range of problems, from the data tsunami to the scarcity of (quality) data for that next AI project.
There are several different types of graph and certainly many have been many attempted definitions of a Knowledge Graph. Here’s ours:
Ultimately, we are wanting to exploit data and their connections or relationships within the graph format in order to surface important and relevant data and information. Without these relationships, the understandings, the stories and the searches around our data tend to dry up fairly quickly. Our world is increasingly connected. So we hope, from an organisational perspective, you are asking: Why isn’t our data connected?!
The term Knowledge Graph was coined by Google on the release of its own Knowledge Graph in 2012. More recently, organisations have been cottoning on to the collective benefits of employing a Knowledge Graph, so much so, that many refer to the Enterprise Knowledge Graph today.
The Enterprise Knowledge Graph is based on a stack ofW3C-ratifiedSemantic Web Technologies. As their names allude to, they form the basis of theSemantic Web. To paraphrase: in 2001 Sir Tim Berners Lee, not content with giving us the World Wide Web for free, pictured a web of connected data and concepts, besides the web of linked documents, so that machines would be able to understand our requests by virtue of known connections and relationships.
These technologies are complex to the layperson and some of them are nearly 20 years old. What’s changed to make Enterprises take note of them now? Well worsening internal data management problems, the need for some knowledge input for most sustainable AI projects and the fact that Knowledge Graph building tools have improved to become collaborative and more user-friendly for the knowledge engineer, domain expert and business executive. The underlying technologies in new tools are more hidden from the end user’s perspective, allowing them to concentrate on encoding their knowledge so that it can be used across enterprise systems and applications. In essence, linking enterprise data.
Thanks to Google’s success in using their Knowledge Graph with their search, Enterprise Knowledge Graphs are becoming recognised as the difference between “googling” and using the sometimes-less-than-satisfying enterprise consumer-facing or intranet search.
The key takeaway here though is that real power of any knowledge graph is in its relationships/connections between concepts. We’ll look into this in more detail next.
EKGs use the simple RDF graph data model at their base. RDF stands for Resource Description Framework – a framework for the way resources or things are described so that we can recognise more easily plus understand more about them.
