Ready to Get to Know the Approach?
Today
Editors and Triplestores.
There are great editors for creating and maintaining ontologies building on various standardized vocabularies, like SHACL, SHex, OWL, RDFS, DCTERMS, and SKOS, to name a few.
And great databases to host knowledge graphs.
problem
What about Quality?
The technical ability to create highly complex ontologies and graphs is no guarantee of high quality. The data and the schema should conform to the common standards of the (hopefully existing) company-wide data strategy. And this is where today’s tools fall short.
Validation and Evaluation
What about SHACL?
Shapes, like SHex or SHACL, allow us to validate data using constraints. But do we know that the rules validating the data are high quality? Do the shapes follow a common standard? SHACL is a great tool. However, it should take the same (or even more) quality consideration than the data.
Our solution
Creating Knowledge Graphs Empirically Better.
Our methodology involves conducting an empirical analysis of the knowledge graph during its lifetime. Utilizing database queries, we thoroughly assess the development of the knowledge graph, ensuring it aligns with set goals. Our evaluation involves measuring various complex metrics spanning almost 200 parameters.
Your solution
Individual as You Are.
Knowledge graphs are as diverse as the data they capture. So is the required evaluation. We tailor the assessment to your needs, ensuring that the assessment does not cover someone’s goals. But yours.
Ready to Get Started?
There has never been a better time than now.