Recall hearing about this approach in training we took. Making it easier to connect key data to analytics. Shape constraint languages.
Graph Database ‘Shapes’ Data By George Leopold in Datanami
A semantic graph database technology vendor is supporting a key specification designed to validate graph-based data against a set of conditions that specify the “shape” of data. The goal is a more agile way of analyzing larger volumes of complex, distributed data.
Franz Inc. said this week its flagship AllegoGraph platform now supports SHACL, the SHApe Constraint Language used to describe “the shape that data should have.” The spec was developed by the World Wide Web Consortium.
The language using a concept known as “triples” to describe data properties. Triples refers to the subject (the thing being described), predicate (properties or relationships of the subject) and object (an intrinsic value such as an integer or text).
SHACL also specifies the number of triples required in a repository along with metadata about the object, including a specified subject and predicate.
Oakland-based Franz said support for SHACL would improve the ability of its graph database to validate data and applications from outside sources while improving interoperability. “Adding SHACL to AllegroGraph helps our customers simplify the complexity of enterprise systems through the ability to loosely combine independent elements, while allowing the overall system to function smoothly,” said CEO Jans Aasman.
The 6.6 version of AllegroGraph includes a data-shaping validation engine used to confirm whether data conforms with desired requirements. SHACL allows a data graph, for instance, to specify the corresponding shapes graph used to describe the link between a given shape and targeted data. .... "
Showing posts with label SHACL. Show all posts
Showing posts with label SHACL. Show all posts
Thursday, August 22, 2019
Sunday, February 10, 2019
Data Governance and Knowledge Graphs
Knowledge Graphs and Data Governance
A Note from TopQuadrant's CEO, Irene Polikoff
As 2018 comes to a close we took a look back at key topics that were of most interest in our conversations with customers, prospects and at the many industry events we attended throughout the year.
A very popular topic continues to be Knowledge Graphs. Looking ahead, we believe that the use of knowledge graphs in the enterprise will be substantially expanded in key areas including data relationship discovery and exploration, semantic interoperability, and especially data governance. Knowledge graphs are foundational to data governance because they catalog diverse enterprise data by capturing both the technical and business context, and meaning of the data through connections across all assets in the enterprise ecosystem.
On the more technical side, we continue to receive a lot of questions about SHACL and its importance to data management. SHACL, a W3C standard for graph data modeling and validation can be used to support many capabilities needed for data governance. I cover some examples in this blog post, “How does SHACL Support Data Governance?” With the addition of SHACL to the standards that support knowledge graphs, it is possible to ensure that the knowledge remains consistent against multiple viewpoints, retains a good quality, and supports the inference of new knowledge that is not explicitly stated in the data.
For these and other reasons I can't cover in this brief note, we believe knowledge graphs are an ideal and, arguably, the only viable foundation for bridging and connecting enterprise metadata silos. For more on TopQuadrant’s views on these topics, see the newly published DATAVERSITY article: “Achieving Data Governance 2.0 with Knowledge Graph Technology.” ... "
http://www.dataversity.net/achieving-data-governance-2-0-knowledge-graph-technology/
https://www.topquadrant.com/2017/10/25/how-does-shacl-support-data-governance/
A Note from TopQuadrant's CEO, Irene Polikoff
As 2018 comes to a close we took a look back at key topics that were of most interest in our conversations with customers, prospects and at the many industry events we attended throughout the year.
A very popular topic continues to be Knowledge Graphs. Looking ahead, we believe that the use of knowledge graphs in the enterprise will be substantially expanded in key areas including data relationship discovery and exploration, semantic interoperability, and especially data governance. Knowledge graphs are foundational to data governance because they catalog diverse enterprise data by capturing both the technical and business context, and meaning of the data through connections across all assets in the enterprise ecosystem.
On the more technical side, we continue to receive a lot of questions about SHACL and its importance to data management. SHACL, a W3C standard for graph data modeling and validation can be used to support many capabilities needed for data governance. I cover some examples in this blog post, “How does SHACL Support Data Governance?” With the addition of SHACL to the standards that support knowledge graphs, it is possible to ensure that the knowledge remains consistent against multiple viewpoints, retains a good quality, and supports the inference of new knowledge that is not explicitly stated in the data.
For these and other reasons I can't cover in this brief note, we believe knowledge graphs are an ideal and, arguably, the only viable foundation for bridging and connecting enterprise metadata silos. For more on TopQuadrant’s views on these topics, see the newly published DATAVERSITY article: “Achieving Data Governance 2.0 with Knowledge Graph Technology.” ... "
http://www.dataversity.net/achieving-data-governance-2-0-knowledge-graph-technology/
https://www.topquadrant.com/2017/10/25/how-does-shacl-support-data-governance/
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