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.
What exactly are knowledge graphs, and what’s with all the hype about them? Learning to tell apart hype from reality, defining different types of graphs, and picking the right tools and database for your use case is essential if you want to be like the Airbnbs, Amazons, Googles, and LinkedIns of the world.
And here is Stardog’s Kendall Clark’s take:
Stardog is the world’s leading Enterprise Knowledge Graph platform. But what is a Knowledge Graph, and why should you want one? Enterprise data is both the disease and its cure. Data will save us, and data will kill us all. At the same time.
Alan Morrison from PwC on how Knowledge Graphs can help collapse the IT Stack
From a keynote presentation delivered at SEMANTiCS 2018 on 12 September 2018. See also the video of this talk at https://lnkd.in/gXxhaMD (starts at 15 minutes…
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
Introducing our Knowledge Graph for encoding relationships and surfacing relevant information Imagine you’re finally getting to take that vacation you’ve dreamed of - three countries, seven cities, thousands of miles. It’s everything you could want and more, right? But where do you start?
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:
Connected Data London, the leading event for those who use the relationships, meaning and context in Data to achieve great things, has announced the lineup for the newly expanded Connected Data London 2018, taking place in London on November 7th, 2018. The conference has expanded its themes and…
Cruce Saunders from [A] elaborates on the relevance of the Semantic Web for enterprise publishers
The Semantic Web is the knowledge graph formed by combining connected, Linked Data with intelligent content to facilitate machine understanding and processing of content, metadata, and other information objects at scale. The Semantic Web leads to smarter, more effortless customer experiences by giving content the ability to understand and present itself in the most useful forms matched to a customer’s need.
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:
With data science and analytics on the rise and under way to being democratized, the importance of being able to find the right data to investigate hypotheses and derive insights is paramount. What used to be the realm of researchers and geeks is now the bread and butter of an ever-growing array of professionals, organizations, and tools, not to mention self-service enthusiasts.
Dan Brickley, schema.org’s mastermind, on RDF and SPARQL
@namedgraph @TigerGraphDB RDF was designed as a data interchange framework; what you do in the privacy of your own database is your own business
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
Rimma Nehme Product Manager and Architect, Azure Cosmos DB Azure Cosmos DB is Microsoft’s globally distributed, multi-model database service for mission-critical workloads. Azure Cosmos DB provides turnkey global distribution with unlimited endpoint scalability, elastic scaling of throughput (at multiple granularities, e.g., database, key-space, tables and collections) and storage worldwide, single-digit millisecond latencies at the 99th percentile, five well-defined consistency models, and guaranteed high availability, all backed by the industry-leading comprehensive SLAs.
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.
Neo4j 3.5 Poised to Power the Next Generation of AI & Machine Learning Systems – Neo4j Graph Database Platform
Latest Neo4j Graph Platform Reveals Context for AI Applications using a Connections-First Approach GRAPHCONNECT, NEW YORK – September 20, 2018 – Neo4j, the market leader in connected data, announced today the upcoming release of Neo4j 3.5, the native graph platform… Read more →
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.
the world’s fastest graph analytics platform for the enterprise, today introduces its latest release, designed to help enterprises harness the power of the fastest and most scalable graph analytics more easily than ever before.
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
For ArangoDB 3.4 we already added 100,000 lines of code, happily deleted 50,000 lines and changed over 13,000 files until today. We merged countless PRs, invested months of problem solving, hacking, testing, hacking and testing again and are super excited to share the feature complete RC1 of ArangoDB 3.4 with you today.
Wrapping up with some hands-on experience on working with graph databases, shared by Expero’s Josh Perryman
We talk with Josh Perryman of Expero about his experiences building highly scalable and performant applications using relational databases, graph databases and sometimes even both at the same time.
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