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Various NoSQL technol ogies, such as graph data models, are becoming a popular approach for data and metadata management due to their flexibility.  This quality, combi ned with evolvability and a focus on relationships, make graphs an exc ellent choice for applications in many areas including social networks, rec ommendation/personalization, fraud detection, 360-degree views (correlating data from multiple sources) and asset management (digital assets, dat a catalogs, and governance).

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In addition to graph databases, you ha ve probably heard the term “knowledge graphs” and how Google an d Microsoft are using them for smart search. A knowledge graph is a special kind of graph, that combines flexibility and evolvability with the followi ng two additional qualities that make them “smart": 

\n\ n Semantic – the meaning of the data is stored al ongside the data in the graph, in the form of ontologies or semantic models . \n Intelligible – semantics of data are explicit a nd include formalisms for supporting inferencing and data validation.\n\n


\nAs a result, knowledge graphs offer a strategic NoSQL approach, ca pable of meaningfully bridging enterprise metadata silos. In this pres entation we will: 

\n\n Provide a brief history of Knowledge Graphs\n Give examples of what they are good for\n Discuss how they provide a powerful platform for integrated data management and governance\n Explore their relationship to:\n \n semantic technology including knowledge representation and machine learning \n the movement towards flexible application APIs using GraphQL\n \n \n\n DTSTART:20190319T113000 SUMMARY:Knowledge Graphs: A Strategic NoSQL Approach for Data Management an d Governance DTEND:20190319T122959 LOCATION: See Description END:VEVENT END:VCALENDAR