GraphNews

My good friend Ed Sverdlin often begins our “Introduction to Graph Databases” presentations with the question “When did computing start?”…

In this work, we propose Knowledge Integration Networks (referred as KINet)

As part of my keynote address at the KM Showcase last week, I wanted to kick off the conference with a view of what I see as the top KM trends today.

For this blog Ahren Lehnert explains why this may be a golden moment for KM to address theoretical benefits in the context of real-world emergencies.

A mathematical theory is presented for the representation of knowledge in the

Knowledge Representation is a Tricky Business. The Choice Between Class and Instance Depends on Your Point of View #knowledgegraph #datamodel #AI #data #tech h/t @palexop

We introduce Kensho, a tool for generating mapping rules between two

#KnowledgeGraphs can be considered to be fulfilling an early vision in Computer Science of creating intelligent systems that integrate knowledge & data at large scale. A Brief History of Knowledge Graph's Main Ideas: A tutorial by @juansequeda http://knowledgegraph.today/paper.html

Why does software development velocity slow as a company scales? See how you can improve your company's knowledge graph with Kubernetes.

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At Stardog, we’re all about making it easy to unify data. That’s why we’ve just open sourced a set of tools to make working with Stardog even easier than before.

based entity recommendation framework

Slides of the LD tutorial at DH2019

Dependencies, like graphs, are everywhere. Achieving a goal is rarely possible in a vacuum and requires collaboration between individuals and/or process...

based models have shown their unique advantages in knowledge graph (KG)

CSV2RDF 2.0 released! https://t.co/mVvzk2LEXdCSV2RDF is a streaming, transforming, #SPARQL-based #CSV to #RDF converter -now with named arguments (shout-out to @picocli) and a Docker image: https://t.co/Ac1CBFLhgM https://t.co/zyN9DW0qqT— Learning SPARQL (@LearningSPARQL) July 11, 2019

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...