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The Decade of the Graph: 2021 Illustrates that Graph is entering the mainstream
The Decade of the Graph: 2021 Illustrates that Graph is entering the mainstream
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 […]
·tigergraph.com·
The Decade of the Graph: 2021 Illustrates that Graph is entering the mainstream
A unified view of Graph Neural Networks
A unified view of Graph Neural Networks
Graph attention, graph convolution, network propagation are all special cases of message passing in graph neural networks.
·medium.com·
A unified view of Graph Neural Networks
ConviviaR Tools: Tagging the Scientific Abstracts with Wikidata Items
ConviviaR Tools: Tagging the Scientific Abstracts with Wikidata Items
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.
·dwayzer.netlify.app·
ConviviaR Tools: Tagging the Scientific Abstracts with Wikidata Items
Bruno Neri on LinkedIn: #graphneuralnetworks
Bruno Neri on LinkedIn: #graphneuralnetworks
"Very Deep Graph Neural Networks Via Noise Regularisation" by Petar Veličković, Yulia Rubanova, Alvaro Sanchez Gonzalez, Jonathan Godwin, et al. Paper...
·linkedin.com·
Bruno Neri on LinkedIn: #graphneuralnetworks
NodePiece: Tokenizing Knowledge Graphs
NodePiece: Tokenizing Knowledge 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…
·medium.com·
NodePiece: Tokenizing Knowledge Graphs
Graph Self Supervised Learning: the BT, the HSIC, and the VICReg
Graph Self Supervised Learning: the BT, the HSIC, and the VICReg
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...
·arxiv.org·
Graph Self Supervised Learning: the BT, the HSIC, and the VICReg
Michael Bronstein on LinkedIn: #GNNs #ICML2021
Michael Bronstein on LinkedIn: #GNNs #ICML2021
#GNNs are related to PDEs governing information diffusion on graphs. In a new #ICML2021 paper with Ben Chamberlain James Rowbottom Maria Gorinova Stefan...
·linkedin.com·
Michael Bronstein on LinkedIn: #GNNs #ICML2021
Harald Sack on Twitter
Harald Sack on Twitter
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
·twitter.com·
Harald Sack on Twitter
BIS Conference on Twitter
BIS Conference on Twitter
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
·twitter.com·
BIS Conference on Twitter
Digitale Akademie on Twitter
Digitale Akademie on Twitter
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
·twitter.com·
Digitale Akademie on Twitter
EDAO on Twitter
EDAO on Twitter
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
·twitter.com·
EDAO on Twitter
Knowledge Graph Benchmarking Report 2021
Knowledge Graph Benchmarking Report 2021
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.
·zenodo.org·
Knowledge Graph Benchmarking Report 2021
Ontology-Based Feature Selection: A Survey
Ontology-Based Feature Selection: A Survey
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.
·mdpi.com·
Ontology-Based Feature Selection: A Survey