Knowledge graphs are network graphs that link related concepts and properties together to create a form of inferencing engine, with knowledge engineering being the programming aspect of graph usage. Explore how knowledge graphs are created and queried, how they are used as part of a broader form of enterprise metadata management, and how they tie into ML and the IoT.
It is easy, when dealing with data, to think of it as something tangible. More precisely, data as water (or as oil) has become such… Read More »DSC Weekly Newsletter 22 March 2022: The Shape of Data
The US Securities and Exchange Commission (SEC), which regulates public company securities, recently proposed its own climate impact reporting requirement. Many US public companies already… Read More »Avoid RegTech myopia with a data-centric approach
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For years, organizations have been working steadily to consolidate their data into a single place via a data warehouse, or more recently, a data lake.… Read More »Tips for Weaving and Implementing a Successful Data Mesh
In the past, Metadata Management is used to know how to use data catalog to find simple data or a book or a periodical in… Read More »Why do you need a metadata management system? Definition and Benefits.
We’re moving at the Cagle house and we’re discovering that, after eight years of living at the same place, one family can collect a lot… Read More »DSC Weekly Digest 01 March 2022: Taxonomists Classify, Ontologists Conceptualize
Data Points Hybrid AI is already overcoming the limitations of single technique approaches Symbolic AI understands actual knowledge, not just data Hybrid solutions open the… Read More »The Hybrid to Give Your AI the Gift of Knowledge
In the last couple of months, I’ve been noticing a gradual shift in the kind of articles that we receive at Data Science Central. We… Read More »DSC Weekly Digest 22 February 2022: Graphology