Knowledge graphs, the technology powering Google, Facebook and Apple, is now unlocking value across the financial sector. Knowledge graphs are transforming critical capabilities and enterprises are increasingly looking to the technology to enhance their tax strategy, perform compliance and improve customer service.

A new generation of note-taking tools helps us quickly organize thoughts as knowledge graphs

Automating language is not magic; it can take years of careful work to piece together what people mean in the context of corporate help desks, HR programs and the like. Such is the work of startup PeopleReign.

While the NLP field has been growing at an exponential rate for the last two years — thanks to the development of transfer based models — their applications have been limited in scope for the job…

Nature Machine Intelligence - The number of graph neural network papers in this journal has grown as the field matures. We take a closer look at some of the scientific applications.

Data on Native communities are not at the levels needed for accuracy in A.I.-driven tools. A group is trying to solve that problem.

Graph data linked with geographic data will provide new means of data discovery, developers were told at a recent conference hosted by Esri.

In the last couple of months, I’ve been noticing a gradual shift in the kind of articles that we receive at Data Science Central. We still get a fair amount of data science content, but increasingly (and admittedly with a bit of encouragement) we’re seeing more articles centered around graphs and semantics. I don’t believe… Read More »DSC Weekly Digest 22 February 2022: Graphology

It is nearly certain that, if you have ever played a 3D video game, watched a CGI-effects-laden movie, or seen increasingly hyperrealistic imagery, you have encountered a scene graph without realizing it. Scene graphs are pervasive in everything from media to medicine, from augmented reality to industrial digital twins, and they are increasingly playing an… Read More »Scene Graphs and Semantics

The Semantic Web has garnered a reputation for complexity among both Javascript and Python developers, primarily because, well, it’s not JSON, and JSON has become the data language of the web. Why learn some obscure language when JSON is perfectly capable of describing everything, right? Well, sort of. The problem that JSON faces, is actually… Read More »Why JSON Users Should Learn Turtle

Just because you’re calling your model a “knowledge graph” doesn’t make it so. True knowledge graphs possess three distinct characteristics.

🧪 From an internal lab project to a project for the future of Dataveyes 🚀 We are very proud to introduce Modality, our analysis and visualisation tool... 36 comments on LinkedIn

Tim Berners-Lee has an interesting habit of coming up with ideas that seem hard to explain at the outset, remain all hard to understand even as they become more implemented and refined, can go for years with only a few die-hard fans becoming convinced that what he is doing is the best thing since sliced… Read More »How Solid Pods May End Up Becoming the Building Blocks of the Metaverse

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Rather than using mathematical graphs and relational databases, Dr. Hart explains the use and development of ontologies using everyday metaphorical scenarios.

Knowledge graphs are a powerful way to assist data scientists to crack hard data problems, but they aren’t as widely known as they could be. Graph database expert Maya Natarajan explains why that’s changing.

Graph Machine Learning (GML) is receiving growing interest within the pharmaceutical and biotechnology industries for its ability to model biomolecular structures, the functional relationships...

Can Machine Learning Do Symbolic Manipulation?  I spent some time over the holidays engaged in a fascinating online conversation. The gist of it was a variation of an argument that has been going on in the realm of artificial intelligence from the time of Minsky and Seymour Papert: Whether it is possible for neural networks to… Read More »DSC Weekly Digest 04 Jan 2022: Can Machine Learning Do Symbolic Manipulation?

How and why the best companies are adopting Graph Visual Analytics, Graph AI, and Graph Neural Networks. By Leo Meyerovich and Ben Lorica. [A version of this post originally appeared on the Graphistry blog.] In this post, we highlight the current state of Graph Intelligence, a new technology category around new tools and techniques forContinue reading "What is Graph Intelligence?"

Artificial Intelligence has a long history of oscillating between two somewhat contradictory poles. On one side, exemplified by Noam Chomsky, Marvin Minsky, Seymour Papert, and many others, is the idea that cognitive intelligence was algorithmic in nature – that there were a set of fundamental precepts that formed the foundation of language, and by extension,… Read More »Where Semantics and Machine Learning Converge

In 2021, knowledge graphs weren't just blossoming-they were blooming-and it was clear that we are now in the knowledge graph stage of KM development

Enterprise Knowledge Graph Platform ROI: Providing readers with a framework to evaluate the potential financial impact of deploying Stardog on their own organizations.

Maya Natarajan, senior director of product marketing at Neo4j, explores the value of knowledge graphs for CIOs

For this blog, Ahren Lehnert, explores knowledge graphs and their punk rhizomatics. A paradigm shift in knowledge organization and sharing.

Interested in using Wikidata & Wiktionary to extract lexicographical information?@sina_ahm created a simple SPARQL query generator that helps non-experts get familiar w/ #SPARQL & create queries to look up words in @wikidata & #Dbnary:👉 https://t.co/gS6JG8EIFb#NLProc pic.twitter.com/9UOCABh3c9— ELEXIS EU (@elexis_eu) December 1, 2021

Curious about the most used ontologies in the agri-food domain? Our #Ontologies Community of Practice has developed a webpage featuring the most popular ontologies & info on those developed by @CGIAR and partners.Find it here: https://t.co/AMVeSHCpdi pic.twitter.com/LmazTo4V2T— CGIAR Platform for Big Data in Agriculture (@CGIAR_Data) December 9, 2021

"... most Entity Linking approaches use Wikidata in the same way as any other knowledge graph missing the chance to leverage Wikidata-specific characteristics to increase quality" > Survey on English Entity Linking on Wikidata https://t.co/JYHJMfVHsO pic.twitter.com/hRxRp3nhnQ— Aaron Bradley (@aaranged) December 10, 2021

Using custom Named Entity Recognition and Relation Extraction models

TempoQR: Temporal Question Reasoning over Knowledge Graphs https://t.co/6SXVnzyG1x pic.twitter.com/LXQW2XYbUb— Aaron Bradley (@aaranged) December 14, 2021