GraphNews

ArangoDB, an open source native multi-model database, is adding a new search feature to let developers efficiently interact with multiple data models by using just one technology and one query language. IDN speaks with ArangoDB CTO Dr. Frank Celler.

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Read about why and how knowledge graph technology helps build networks of interwoven digital objects in the world of cultural heritage.

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Don't try to put the cart before the horse: realize that efficient data preparation (and thus interoperable standards) and data quality, especially in the enterprise environment, are a basic requirement for all applications of artificial intelligence. The development of competences and experts in th

We are going to show how we import the W3C Organizational Ontology into Neo4j using the neosematics tools.

like experience for quickly importing CSV data into Stardog. To get started, just choose a database under Studio’s”Databases”tab and click on”ImportCSV.” Studio’s wizard will extract the headers (if any) from the CSV file you supply and will let you choose both a name for the class of data that the CSV represents(i.e.,the type of thing to which each row of the CSV corresponds) and the column that should be used for generating unique identifiers for instances of that class. To help you choose a truly unique identifier, the wizard will also show you just how distinct the data in each column of the CSV is, and will indicate whether or not the column you’ve chosen is likely to be a good one with respect to data integrity. Data Exploration

"We propose constructing one #knowledgegraph per query & show this method compresses information and reduces redundancy" > Improving long-form question answering by compressing search results / Angela Fan @facebookai h/t @aaranged https://ai.facebook.com/blog/research-in-brief-training-ai-to-answer-questions-using-compressed-search-results/

Learn about how AstraZeneca visualized patient journeys, answered important questions about prescriptions and diagnoses, and improved patient outcomes.

This article looks at the knowledge graphs of five diverse tech companies, comparing the similarities and differences in their respective experiences of building and using the graphs, and discussing the challenges that all knowledge-driven enterprises face today. The collection of knowledge graphs discussed here covers the breadth of applications, from search, to product descriptions, to social networks.

Five diverse technology companies show how it's done.

.@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