GraphNews

I love that SPARQL is the language used to compare the mentions of all the other programming languages here. https://t.co/PMclq0YViC— Learning SPARQL (@LearningSPARQL) November 30, 2018

“Look at what happens when you start playing around with #SPARQL queries… #Wikibase https://t.co/Q9pLZ8tS4Y”

Step-by-step tutorial: #RDF-ize tabular data, publish it and build an Angular front-end. Well done @ElvinDechesne! Clear and comprehensive explanations how to use OntoRefine for ETL in Part 1! #SPARQL #KnowledgeGraph https://t.co/ZF3XDw5wvV— Atanas Kiryakov (@kiryakov_ak) March 5, 2020

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This is a summary of a short talk I gave internally at the ODI to help illustrate some of the important aspects of data standards for non-technical folk. I thought I’d write it up here too, i…

How do search engines retrieve results? How can the #UnitedNations published content be more relevant? #LinkedData is structured #data interlinked w other data, making it more useful/discoverable through #semantic queries. UN launches linked data platform

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On the way to more powerful GNN. Source.There are two paradigms for graph representations: graph kernels and graph neural networks. Graph kernels typically create an embedding of a graph, based on decomposition, in an unsupervised manner. For example, we can count the number of triangles or more generally triplets of each type a graph has and then use these counts to get embeddings. This is known to be an instance of a graphlet kernel.

Traditional methods for link prediction can be categorized into three main types: graph structure feature-based, latent feature-based, and explicit feature-based. Graph structure feature methods leverage some handcrafted node proximity scores, e.g., common neighbors, to estimate the likelihood of links. Latent feature methods rely on factorizing networks' matrix representations to learn an embedding for each node. Explicit feature methods train a machine learning model on two nodes' explicit attributes. Each of the three types of methods has its unique merits. In this paper, we propose SEAL...

This open access book shows the factors linking information flow, social intelligence, rights management and modelling with epistemic democracy, offering licensed linked data along with information ab

Overview

GQL is an effort to standardize property #GraphDB query language. GQL project lead leaving #Neo4j, WG3 is now responsible. "There is no certainty about the success of the GQL project, but there are good objective and subjective grounds for optimism"

#smartcity stakeholders must build #datagovernance & management ready for all categories of #data. Required:assessment of expected current/future categories, scalable, flexible, modular design. Shared, unified, standards-based graph data model: #ontology

Graph databases are finding a place in analytics applications at organizations that need to be able to map and understand the connections in large and ...

LinkedIn has been forced to ‘pause’ a feature in Europe in which the platform emails members’ connections when they’ve been ‘mentioned in the news’. This follows a number of data protection complaints after LinkedIn’s algorithms incorrectly matched members to news articles — triggering an internal review of the feature. LinkedIn told us it subsequently decided […]

In this release of Linkurious Enterprise 2.7, we focused on helping you automate investigations repetitive or complex workflows.

>)You may have read that companies such as Amazon, Facebook, Microsoft, JPMorgan and Bank of America have made large investments to develop their own proprietary knowledge graphs, to make “strategic use of data and extend business boundaries[1]”.How is this relevant to the “SQL World”?What does this have to do with you, the SQL/relational database professional? Why should you care that there is a new world of databases that is alien to most relational databases experts and users?For the near future maybe you shouldn’t care. After all, about 80% of the database infrastructure in the world is relational, so for you SQL is a sure bet.But wait, here’s big data, ever growing big data. Big data is complex, it has variety and it is difficult to

The coronavirus pandemic is disrupting universities and research institutes across the world. But the same institutions are also working very hard to find out how the disease can be stopped and its effects mitigated.

Lee — to pioneer ways of giving people control over their own personal data. Solid maintains that people should be free to share “whatever they want with whomever they want.” The key to this mechanism of control is a personal data “Pod”

When it comes to understanding predictions generated by machine learning on graphs, one of the biggest questions we’re left with is; why…

I started out 2018 with a new focus in graph technologies. This year I will continue to look at graph technologies and how they are…

A Knowledge Space is your status in a graph of learning concepts

Curious about understanding the performance of a #GraphDatabase ?We investigated 35 types of operations and performed experiments on all the major #GraphDb in this #vldb paper Read more on https://t.co/13yNkohAIQ https://t.co/9G8DFlhHDb— M. Lissandrini (@Kuzeko) February 13, 2019

Slides for my summer school talk on ML and Embeddings for Knowledge Graphs

#MachineLearning on Graphs becomes a first-class citizen at #AI conferences while being not that mysterious as you may have imagined @michael_galkin checks out the goodies brought by NeurIPS 2019 #knowledgegraph #deeplearning #NLP #research h/t @aaranged