GraphNews

.@TDataScience talks about inference on #SemanticWeb and how to apply in a local #graphDB. What is Inference? What is it used for? Types of the procedure, Graph #Database & #Ontology, Inference in a Database #knowledgegraph #semantics #tutorial

After more than a decade, the supply-driven approach to publishing public (open) data has resulted in an ever-growing number of data silos. Hundreds of thousands of datasets have been catalogued and can be accessed at data portals at different administrative levels. However, usually, users do not think in terms of datasets when they search for information. Instead, they are interested in information that is most likely scattered across several datasets. In the world of proprietary in-company data, organizations invest heavily in connecting data in knowledge graphs and/or store data in data ...

Cicero is an #AI platform used to reduce reporters’ manual work while helping find connections, providing more transparency to readers. When a #journalist searches one of the three output options is a #knowledgegraph @gartht1 h/t @aaranged #media #tech

Virtual assistants do their jobs better thanks to Wikidata, which aims to (eventually) represent everything in the universe in a way computers can understand.

Discover how the big data company Thomson Reuters used Neo4j to develop a real-time intelligent graph recommendation engine for financial analysts.

Part 3 in a series about Knowledge Graphs

To understand trends as they’re happening, @pinteresteng needs to understand content & categories. To do that, they built a taxonomy-based knowledge management system that enables content understanding in a highly efficient way #knowledgegraph #AI #data

Song Cui and Dhananjay Shrouty | Software Engineers, Content KnowledgeInterest Taxonomy at PinterestWe recently began rolling out the beta version of Pinterest Trends, a new tool that gives a view...

Bill Slawski is an SEO legend.

Interview with Oshani Seneviratne – Healthier eating for diabetics with the RPI + IBM Food KG Interview – RPI Food KG with Oshani Seneviratne Tell us about your project. The food knowledge graph (or foodkg) is a joint project between Rensselaer Polytechnic Institute and IBM (part of the IBM AI Horizons Network). The project’s official […]

This short video illustrates weaving #linkeddata image descriptions from an @internetarchive collection and demonstrates machine inference with @dbpedia & @wikidata. An intro for @Imagesnippets #SEO #semantics #knowledgegraph #EmergingTech

After building and selling Heyzap back in 2016 I had free reign to dig into the question of “what should I really be working on?” and how to create something that would have a lasting impact on society. In part of this journey I’ve had the privilege to invest

Visually explore rich #datasets w/ #GPU & graph computing, no manual code. Add GPU experiences to #apps w/ Python or JavaScript @Graphistry for individuals and startups: same #software, 8X cheaper #graph #data #analytics #dataviz [LINK]https://www.graphistry.com/blog/introducing-graphistry-for-individuals-and-startups[/LINK] [IMAGE]https://www.graphistry.com/wp-content/uploads/2019/12/biogrid.gif[/IMAGE]

Amazon Neptune is a fast, reliable, fully managed graph database, optimized for storing and querying highly connected data. It is ideal for online applications that rely on navigating and leveraging connections in their data. Amazon Neptune supports W3C RDF graphs that can be queried using the SPARQL query language. It also supports Apache TinkerPop property […]

Until now the only way to run Graph Algorithms on Neo4j has been to learn Cypher. The Graph Algorithms Playground changes all that.

Introducing the Neo4j Graph Data Science plugin with examples from the “Graph Algorithms: Practical Examples in Apache Spark and Neo4j” bookIn the past couple of years, the field of data science has gained much traction. It has become an essential part of business and academic research. Combined with the increasing popularity of graphs and graph databases, folks at Neo4j decided to release a Graph Data Science (GDS) plugin. It is the successor of the Graph Algorithms plugin, that is to be deprecated.Those of you who are familiar with Graph Algorithms plugin will notice that the syntax hasn’t changed much to allow for a smoother transition. To show what has changed, I have prepared the migration guides in the form of Apache Zeppelin notebooks that can be found on GitHub.Neo4j connector for Apache Zeppelin was developed by Andrea Santurbano, who also designed the beautiful home page notebook of this project and helped with his ideas. In the migrations guides, we used the ex

#KnowledgeGraphs & #Applications. What makes a KG what it is, is that, unlike a regular database that gets populated & stays dormant, a KG is supposed to re-purpose itself, provide new insights and inferences #datascience #AI #analytics @AnalyticsVidhya

Introduces core concepts of Linked Open Data, including URIs, ontologies, RDF formats, and a gentle intro to the graph query language SPARQL.

authored by Intuit Distinguished Engineer and Architect, Jay Yu, Distinguished Engineer and Director, Kevin McCluskey, and Distinguished Data Scientist, Saikat Mukherjee.

3DSculptor/Getty Images Every big company now manages a proliferation of sites, apps, and technology systems for interacting with buyers and managing everything in the business, from customers and clients to inventory and products. These systems are spitting out data continuously. But even after multiple generations of investments and billions of dollars of digital transformations, organizations struggle to use that data to improve customer service, reduce costs, and speed the core processes that provide competitive advantage. AI was supposed to help with that. But as an executive at a major life insurance company recently told me (Seth), “Every one of our competitors and most of the organizations of our size in other industries have spent at least a few million dollars on failed AI initiatives.” Why? My 20 years of experience working with companies on their information technology have shown me the reason: because promises of AI vendors don’t pay off unless a company’

SPARQL 1.2 Community Group. Contribute to w3c/sparql-12 development by creating an account on GitHub.

On the Role of #KnowledgeGraphs in Explainable #AI: A #MachineLearning Perspective @freddylecue @Inria h/t @aaranged @iswc_conf #iswc2019 #semex2019 #research http://www-sop.inria.fr/members/Freddy.Lecue/presentation/ISWC2019-FreddyLecue-Thales-OnTheRoleOfKnowledgeGraphsInExplainableAI.pdf

ClaimsKG, a #knowledgegraph of fact-checked claims, which facilitates structured queries about their truth values, authors, dates, journalistic reviews and other kinds of metadata. Harvests data from popular fact-checking sites, annotates w @DBpedia

With our semantic chatbot, semantize your data without even having to understand what data semantization is!

Microsoft Academic Knowledge GraphA #SPARQL endpoint to and #RDF dumps of8 Billion Triples of Scholarly Data.https://t.co/bs6pXoh9Z7https://t.co/orZEq9Dt8Jhttps://t.co/IUP8FQWk5j pic.twitter.com/KBvtdAOzwd— Ivo Velitchkov (@kvistgaard) January 8, 2019

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We are proud to announce the release of #JanusGraph 0.4.0 with CQL OLAP support, performance improvements for pre-fetching of properties, and many updated dependencies: TinkerPop, Cassandra, HBase, Bigtable, and BerkeleyDB! Download now: https://t.co/tEkjTojUs2— JanusGraph (@JanusGraph) July 11, 2019

We just launched official #JanusGraph @Docker images to simplify production deployments and testing! Check out the images at https://t.co/Vo44Bokwpu and see the docs for more info: https://t.co/VLaLwR6jcI— JanusGraph (@JanusGraph) May 8, 2019