One Ontology, One Data Set, Multiple Shapes with SHACL. Tara Raafat

Data integration, data interoperation and data quality are major challenges that continue to haunt enterprises. Every enterprise either by choice or by chance has created massive silos of data in different formats, with duplications and quality issues.
Knowledge graphs have proven to be a viable solution to address the integration and interoperation problem. Semantic technologies in particular provide an intelligent way of creating an abstract layer for the enterprise data model and mapping of siloed data to that model, allowing a smooth integration and a common view of the data.
Technologies like OWL (Web Ontology Language) and RDF (Resource Description Framework) are the back bone of semantics for knowledge graph implementation. Enterprises use OWL to build an ontology model to create a common definition for concepts and how they are connected to each other in their specific domain.
They then use RDF to create a triple format representation of their data by mapping it to the Ontology. This approach makes their data smart and machine understandable.
But how can enterprises control and validate the quality of this mapped data? Furthermore, how can they use this one abstract representation of data to meet all their different business requirements? Different departments, different LoBs and different business branches all have their own data needs, creating a new challenge to be tackled by the enterprise.
In this talk we will look at how the power of SHACL (SHAPES and Constraints Language), a W3C standard for defining constraint sets over data; complements the two core semantic technologies OWL and RDF. What are the similarities, the overlaps and the differences.
We will talk about how SHACL gives enterprises the power to reuse, customize and validate their data for various scenarios, uses cases and business requirements; making the application of semantics even more practical.
Presentation by Tara Raafat at Connected Data London 2019: connected-data.london/session... ---
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[@carocardozo1507]

Wow she was so good explaining and real-world examples, loved it. Thank you!

[@logicalNerd]

Very informative video. Although, it was a really dumb idea to cover 40% of the frame with endorsements and 10% on the banner while showing the real content on the rest.

[@rkjessop]

Corporate sponsorship is a necessary aspect of what we do; 40% during the entire presentation was not a well-thought out idea.

[@subhashisdas81]

Very informative indeed. There was a problem with the ontology design. OWL can handle this kind of scenario if we treat employee (or student) as the role of a person and not a subclass of a "person" class which carry specific property only for a given time frame. Is to say that "employee" is not a person in his/her whole life but for a specific period. I think the OntoClean methodology can provide more information regarding what I am saying.

[@subirdas0]

Absolutely smashing !

[@ConnectedData]

Book your seat for our next Online Meetupl June 29th! 👉 bit.ly/3fd9dzQ

[@luisramos1977]

when I classify a human a kind of person, or into a category of age or income, and then run a reasoner to prove that somebody is in that category, then I am doing a data validation.