GraphNews

Mapped, which simplifies access to building assets via a digital twin API, raised $6.5 million in a seed II round.

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.

Our e-book aims to shed light on what we believe is a real game-changer for those looking to improve upon simplistic answers sometimes arrived at by using traditional ML algorithms and approaches. We show how you are able to combine the power of both graphs and ML (in a variety of different ways) to help you arrive at better answers compared to using standard ML approaches alone.

TigerGraph came out of stealth in 2017, and every year since has been coined “The Year of the Graph” by experts, journalists, and market watchers due to the accelerating momentum.  2018, 2019, and 2020 each had incremental “Year of the Graph” potential. In those years, more and more enterprises adopted graph at scale for increasingly […]

With data scientists in high demand, NASA is pursuing a pioneering approach to filling its skills gaps.

Graph attention, graph convolution, network propagation are all special cases of message passing in graph neural networks.

Putting value on relationships.

Here I am trying to build a script that process the short scientific texts (abstracts) and finds Wikidata items corresponding to the terms. An interactive and editable table is also created to allow an editor to validate the found matches and find other related items. A bit amateurish attempt by a Wikidata newbie.

Commentary: Relational databases aren't dead, but database innovation is cropping up in all sorts of places now, including graph databases.

Fridays give away! Graph Machine Learning: Take graph data to the next level by applying machine learning techniques and algorithms. Like and comment ... 75 comments on LinkedIn

"Very Deep Graph Neural Networks Via Noise Regularisation" by Petar Veličković, Yulia Rubanova, Alvaro Sanchez Gonzalez, Jonathan Godwin, et al. Paper...

Iconology is a branch of art history that investigates the meaning of artworks in relation to their social and cultural background. Nowadays, several interdisciplinary research fields leverage...

We describe a novel method for identifying and extracting conceptual components from domain ontologies, which are used to understand and compare them. The method is applied to two corpora of...

Explainability techniques for Graph Neural Networks still have a long way to go compared to explanations available for both neural and decision decision tree-based models trained on tabular data....

Knowledge graphs are getting more mature and very practical in many different domains. From enabling data mesh architecture to disaster resilience, knowledge...

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

Mapping each node to an embedding vector results in enormously large embedding matrices. Is there a way to have a fixed-size vocabulary of…

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

Self-supervised learning and pre-training strategies have developed over the last few years especially for Convolutional Neural Networks (CNNs). Recently application of such methods can also be...

#GNNs are related to PDEs governing information diffusion on graphs. In a new #ICML2021 paper with Ben Chamberlain James Rowbottom Maria Gorinova Stefan...

SUMMARY

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

Analytical #exploration of large #KnowledgeGraphs requires study of new methods for efficient query execution.Selecting which view to materialize is one of the challenges on the path for faster exploratory analytics.In this demo we shed some light on this interesting problem https://t.co/7ylrZxSHye— EDAO (@EDAO_eu) June 18, 2021

Learn how to visualize your intellectual property protection holistically to discover the gaps and evaluate their effects on dependent products and revenues.

Knowledge technologies and semantic standards are changing the nature of data management – but the issues, obstacles and use cases that define the industry are often described in anecdotal terms. To address that information gap, the Knowledge Graph Conference commissioned this study to begin to quantify the size, dynamics and direction of the industry.  We hope you find the report and our extended analysis useful.

Winners of the OGB-LSC graph machine learning challenge have just been announced at https://lnkd.in/dP83-fV Congratulations to the winning teams from Baidu...

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.