GraphNews

Basic Introduction to what Graph Analytics is…

Graph databases are the fastest-growing category in all of data management. Here's how to pick one.

Must-read papers on graph neural networks (GNN). Contribute to thunlp/GNNPapers development by creating an account on GitHub.

The Stardog 7.6.0 release can load data at million triples per second speed.

Read about how knowledge graphs can unlock meaning and thus create a smart future-proof living repository of scientific data and its relationships.

Google's Knowledge Graph algorithm currently updates every 2 to 3 weeks, affecting 60-80% of entities. Here's what SEOs need to know.

The still young discipline of the management and governance of knowledge graphs (KG) is gradually beginning to consolidate on the basis of concrete project ex…

A detailed definition with examples for RDF-Star (also known as “RDF*”), which allows descriptions to be added to edges in a graph.

The unification of statistical (data-driven) and symbolic (knowledge-driven) methods is widely recognised as one of the key challenges of modern AI. Recent years have seen large number of...

Learn what graphs, the graph theory, and graph databases are in this week’s #GraphCast, featuring two slick videos explaining these concepts.

This blog highlights some of the new automation features that Cambridge Semantics is building into our scalable knowledge graph platform - Anzo.

https://lnkd.in/dTvZzMb "A curated list of various semantic web and linked data resources. To add something to the list please either submit a pull request...

personalized recommendations #8

A talk about content models, good communications protocols between information and the link between content and graphs.

160 SPARQL endpoints, indexing of stuff in GitHub repositories: interesting work by Vincent Emonet at @UM_IDS: https://t.co/6Jh93YEvRP (file PRs for missing resources here: https://t.co/M5d94NgIrn)— Egon Willighⓐgen (@egonwillighagen) February 27, 2021

2020 Was The “Year of the Knowledge Graph” 2020 was undeniably the “Year of the Knowledge Graph.” 2020 was the year that Gartner put Knowledge Graphs at the peak of its hype cycle. It was the year where 10% of the papers published at EMNLP referenced “knowledge” in their titles. It was the year over […]

In this post i’ll give you an overview of some similarity metrics I’ve discovered when working with WordNet. Even though they were originally proposed as linguistic similarity metrics, …

"Graph Convolutional Embeddings for Recommender Systems" by Paula Gómez Duran, Alexandros Karatzoglou et al. Paper: https://lnkd.in/dHskknV #graphnn #...

This article explores the role of knowledge graphs in data fabrics. If you want to learn more about this topic, please join Forrester…

Interactively Constructing Knowledge Graphs from Messy User-Generated Spreadsheets / Markus Schröder et al. @DFKI https://t.co/XQ6ZNbOm6z pic.twitter.com/lkMnLGka4L— Aaron Bradley (@aaranged) March 8, 2021

https://t.co/ooIuC1elTy version 12 is out - thanks to all who collaborated on this! https://t.co/rCzFkiwbBo has the details. In this edition https://t.co/ooIuC1elTy can now distinguish 6 kind of media-authenticity problem for reviewing images and videos; ... pic.twitter.com/a6yZv0ayDR— Dan Brickley (@danbri) March 8, 2021

It gives a framework and place to begin, recognizing that no outreach plan will work for everyone so it’s necessarily an iterative, step-by-step process.

New @orkg_org release today featuring a new contribution editor, where you can directly add a comparison table to capture the state-of-the-art for a particular research problem: https://t.co/nhZ7Gjzilh pic.twitter.com/MJmLp9bvIi— Sören Auer (@SoerenAuer) March 8, 2021

“This library provides a prolog interface to SPARQL queries. It allows logic program queries to be compiled to SPARQL, and then executed on a remote SPARQL server.” https://t.co/3YaKL1AJkq— Learning SPARQL (@LearningSPARQL) March 8, 2021

GNN is the new hype but revisiting deep learning applied to graphs is a great opportunity to learn some fundamental concepts in machine learning: language...

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Meaning is core to language because the meaning of a sentence determines the forms of words and phrases that are selected and vice versa…

Graph applications are ubiquitous, learn how to use Machine Learning with them!