GraphNews

Stanford is proud to organize a workshop that  brings together leaders from academia and industry to showcase recent advances in Graph Neural Networks.... 45 comments on LinkedIn

Dr. Alicia Frame, director of graph data science at Neo4j, explains how graph technology could ameliorate the end of the third-party cookie

Yesterday I presented the paper "Graph? Yes! Which one? Help!" at the SEMANTiCS 2021 conference (co-authored with several colleagues from the Neptune team...

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I'm relatively old school, semantically speaking: my first encounters with RDF was in the early 2000s, not long after Tim Berners-Lee's now-famous article in Scientific American introducing the Semantic Web to the world.

Graph database query languages are growing, along with graph databases. They let developers ask complex questions and find relationships.

Types x Individuals, Instantiation x Specialization. These are among the most fundamental distinctions in Conceptual Modeling. Still, they seem to elude... 23 comments on LinkedIn

Leveraging more data sources using machine learning on graphs to enhance item suggestions for Decathlon members

Content management and its many challenges Content is inherently diverse, written for and understandable only by a specific audience, often multilingual, scattered across silos and different systems, and regularly ambiguous and therefore difficult to understand. Even humans, who are actually experts

Gartner has put “Knowledge Graphs” at the peak of inflated expectations. If you are a Knowledge Graph software vendor, this might be good news. Companies will be buying knowledge graphs without knowing what they are. I’m reminded of an old cartoon of an executive dictating into a dictation machine: “…and in closing, in the future […]

And the Quest for a New Business Model for Open Source Projects

Researchers are turning to the mathematics of higher-order interactions to better model the complex connections within their data.

Very honored to have been invited by Jesús Barrasa Amy Hodler and Jim Webber to write the foreword for their book "Knowledge Graphs Data in Context for...

As data grows at an exponential rate, TigerGraph aims to make its graph database an attractive choice for data management because of its ability to scale.

🤖 A Python library for learning and evaluating knowledge graph embeddings - GitHub - pykeen/pykeen: 🤖 A Python library for learning and evaluating knowledge graph embeddings

Reply to "Why I Don't Use OWL Anymore" by Irene Polikoff - In recent years, many linked data experts have reflected on the use of SHACL in…

This course is aimed at digitization managers and data scientists who are responsible for datasets and knowledge management and helps you to improve the handling of information. Using various knowledge graph tools as examples, learn how to network your datasets and build a digital knowledge base in this course.

Last year, we have announced our collaboration with Google Maps on deploying a graph neural network for estimating expected travel times (ETAs), which ... 31 comments on LinkedIn

Enterprises often find difficulty in applying insights from AI because of a lack of context, this is where knowledge graphs come in.

Ensuring the success of big graph processing for the next decade and beyond.

Within financial services, a lot of firms are still stuck in the past, using decades old tech to compete on a whole new data-driven playing field. These firms face growing challenges from regulators, and as part-fossilized data dinosaurs, they are threatened with extinction. In this paper, Capco’s Mark Kitson explores how knowledge graphs could be the answer to this problem.

How combining three emerging technologies is driving innovation

Ecstatic for our #IJCAI2021 research paper (14% acceptance)! Presenting TEC, a dynamic time-evolving graph neural network for capturing speaker language...

“Tutorial 7: Graph Neural Networks”...

Data may be the world’s most valuable resource, especially in financial services, which depends on harnessing insightful data. Structures are needed to make growing banks of data decipherable and useful, such as knowledge graphs—interlinked descriptions of relevant objects and their relationships and powerful aids in understanding data.