By now, we have an understanding of what a graph is and learned some of the methods in traversing them.

A few slides on the applications of Graph Neural Networks https://lnkd.in/gyvkHrr...

Deep Learning on Graphs for Natural Language Processing (DLG4NLP) is a very fast-growing area in recent years. Welcome to attend our DLG4NLP tutorial...

Devs: Semantic Web is so dead Amazon: provides a Linked Data API for Alexa with JSON-LD support https://t.co/ghLLiFOsWk

This is the first article in a four-part series on graph theory and graph neural networks. It explains graph theory in machine learning, and how it’s changed the game.

Using R to extract user data from the Yelp API and create network graphs

There's a new query language in town, PathQuery - one "developed to scale with Google's query and data volumes as well as its internal developer community...

After your first steps with SPARQL we are now explaining more sophisticated SPARQL queries on the example of @wikidata in today's #ise2021 lecture#knowledgeGraphs #SemanticWebhttps://t.co/ZxxZftV7gd pic.twitter.com/I3T1TrX6P6— Harald Sack (@lysander07) June 18, 2021

3rd day of #BIS2021 started with a keynote presentation: Industrial #KnowledgeGraphs in Practice by Sonja Zillner from @siemens https://t.co/9K9BSRYqq0 pic.twitter.com/VR42aQOggQ— BIS Conference (@BISconf) June 16, 2021

SPARQL like a pro! Learn about SPARQL federated queries, variable bindings and aggregations in today's #ise2021 lecture#SemanticWeb #knowledgegraphshttps://t.co/pD31Uh6tHt pic.twitter.com/d5Pju2bQYm— Harald Sack (@lysander07) June 19, 2021

The Semantic Web emerged as an extension to the traditional Web, adding meaning (semantics) to a distributed Web of structured and linked information. At its core, the concept of ontology provides the means to semantically describe and structure information, and expose it to software and human agents in a machine and human-readable form. For software agents to be realized, it is crucial to develop powerful artificial intelligence and machine-learning techniques, able to extract knowledge from information sources, and represent it in the underlying ontology. This survey aims to provide insight into key aspects of ontology-based knowledge extraction from various sources such as text, databases, and human expertise, realized in the realm of feature selection. First, common classification and feature selection algorithms are presented. Then, selected approaches, which utilize ontologies to represent features and perform feature selection and classification, are described. The selective and representative approaches span diverse application domains, such as document classification, opinion mining, manufacturing, recommendation systems, urban management, information security systems, and demonstrate the feasibility and applicability of such methods. This survey, in addition to the criteria-based presentation of related works, contributes a number of open issues and challenges related to this still active research topic.

Do Transformers Really Perform Bad for Graph Representation? Ying et al.: https://lnkd.in/dNaPiEf #Transformers #GraphRepresentation #DeepLearning...

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A collection of important graph embedding, classification and representation learning papers with implementations. - benedekrozemberczki/awesome-graph-classification

I plan to add this quote to a slide someday: "“Linked Data” is a broad marketing euphemism for RDF that emphasises some but not all of its strengths, such as the ease of data merging across loosely coupled systems. But it is not a technical term or a W3C standard as such."

OpenActive is a community-led initiative in the sport and physical activity sector in England. It’s goal is to help to get people healthier and more active by making its easier for people to …

Can anyone tell me about existing projects that pull RDF as JSON-LD (especially https://t.co/Rxky83WuLZ data) from web pages and then do things with it?

Graph Theory in Machine Learning, and How it’s Changed the Game

Announcing preview availability of https://t.co/587lbNTOjD - Schema Markup Validator https://t.co/QvvuEGHtqd Guest post from @rrlevering #schemasmv https://t.co/AVi6ZJoC77

Discover the top 5 graph analytics takeaways from Gartner's Data & Analytics Summit, from creating context to improving communication.

The performance of applications, such as personal assistants, search engines, and question-answering systems, rely on high-quality knowledge bases, a.k.a. Knowledge Graphs (KGs). To ensure their...

Stanford / Winter 2021

Hierarchical Graph Neural Networks - Over the recent years, Graph Neural Networks have become increasingly popular in network analytic and beyond. With...

Guided sampling for large graphs Taking a small break from social networks. In this paper, they suggest an efficient and simple graph sampling method...

Introduction to basic concepts and applications of graph mining

Social or professional cliques often form around a shared task or interest. For example, you and fellow graduate students from the same program who go to a conference together form a clique.

@nightrose @danbri (reviving this old conversation here 👻) is it there a tool or an API that I can query to describe a #Wikidata entity using the https://t.co/IoQttEpJyY vocabulary? 🧐 cc @danbri @thadguidry @cyberandy

We're excited to see @qian_yifan's article "Geometric graphs from data to aid classification tasks with Graph Convolutional Networks" published in @Patterns_CP. Congratulations!https://t.co/i5qGBHq6Cw— CSSI (@KelloggCSSI) April 21, 2021

How does the Kudzu NFT virus spread in the Ethereum network? 🕸️Explore the virus outbreak on @graphcommons /2 https://t.co/US323sZUti @billyrennekamp @lewdmechanical @hxrts @MPtherealMVP pic.twitter.com/AXMWXMaFpY— Burak Arikan (@arikan) April 22, 2021