Getting knowledge graph semantics and definitions right, semantic web standards used in the real world, by Google no less, and ArangoDB, Azure CosmosDB, Neo4j and TigerGraph announcing new versions.
By now you probably know that knowledge graphs are in Gartner’s Hype Cycle. But how does one actually define a knowledge graph? My take on ZDNet.
And here is Stardog’s Kendall Clark’s take:
Alan Morrison from PwC on how Knowledge Graphs can help collapse the IT Stack
Did you know Airbnb also has a knowledge graph? You can read about it here. Note the insightful comment on the nuances of building knowledge graphs in the real world from LinkedIn’s manager of taxonomy
Want to know how knowledge graphs work in the real world? How to handle semantics at web scale, how this helps with data governance, how to evaluate graph databases, or how graphs and AI can work together? Then this is the event for you — check out the program officially announced:
Cruce Saunders from [A] elaborates on the relevance of the Semantic Web for enterprise publishers
Google just expanded search, so now you can also search for data. Besides being very useful, this also shows how schema.org and semantic web standards work in real life:
Dan Brickley, schema.org’s mastermind, on RDF and SPARQL
Azure CosmosDB announced new capabilities at Microsoft Ignite 2018. None of those is graph-specific, but things like multi-master at global scale should come in handy regardless
Neo4j also announced a new version, 3.5, at Graph Connect NYC. Main new features in v3.5, available in Q4 2018, are full-text search and new graph algorithm implementations.
A few days before Neo4j, TigerGraph also announced a new version. TigerGraph has added integration with popular databases and data storage systems, announced a github repository to host open source connectors, added support for graph algorithms, and a Neo4j migration kit.
ArangoDB has a new release in the works too: 3.4. A release candidate is available, and main new features are search, support for GeoJSON and Google S2 index, performance improvements via query profiling and streaming cursors, and making RocksDB the default storage engine
Wrapping up with some hands-on experience on working with graph databases, shared by Expero’s Josh Perryman
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Originally published at linkeddataorchestration.com on October 1, 2018.