GraphNews

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?

Heard of Notion.so? Most people can't quite put their finger on what makes this popular collaborative workspace different than other tools they've used... 13 comments on LinkedIn

By now, if you’ve been following this series, you may have learned a bit about graph theory, why we care about graph structured data in data science, and what the heck a “Graph Convolutional Network”…

Using neural networks, the site is able to find images—and ads—that will catch the consumer’s eye.

Taxonomies are crucial for businesses and institutions to handle bigger amounts of data. Manually organizing thousands of concepts into a knowledge tree has so far been the only way to do this. Aside

Posted by Siamak Shakeri, Staff Software Engineer and Oshin Agarwal, Research Intern, Google Research Large pre-trained natural language...

Causaly, the London-based company that allows researchers and specialists to intuitively map and navigate the intricate landscape of biomedical research, has raised $17 million from investors to grow its team and expand into new markets.

GraphQL is a query language for APIs. The APIs that provide a GraphQL interface to query data are called GraphQL APIs. They provide an easy-to-use interface to query data from different sources in a single API call.

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

We are thrilled to announce that Causaly has completed a new financing round of $17m

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*GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training* by Tianle Cai, Keyulu Xu, Di He, Tie-Yan Liu, Liwei Wang et al. What ...

We present Arianna+, a framework to design networks of ontologies for representing knowledge enabling smart homes to perform human activity recognition online. In the network, nodes are ontologies...

This is what happens when you bulk add keywords without paying attention to the details. If you’re searching for a specific result in a database or on... 43 comments on LinkedIn

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

Graph clustering aiming to obtain a partition of data using the graph information, has received considerable attention in recent years. However, noisy edges and nodes in the graph may make the...

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This research talks about using Random Walk inspired Anonymous Walks as graph units to derive feature-based and data-driven Graph Embeddings in an unsupervised...

This is a guest post by Russell Waterson, Knowledge Graph Engineer at Data Lens Ltd. Customers use knowledge graphs to consolidate and integrate information assets and make them more readily available. Building knowledge graphs by getting data from disparate existing data sources can be expensive, time-consuming, and complex. Project planning, project management, engineering, maintenance and […]

A new benchmarking study released at this week’s The Knowledge Graph Conference illustrates how large corporates are starting to embrace…

Over the recent years, Graph Neural Networks have become increasingly popular in network analytic and beyond. With that, their architecture noticeable diverges from the classical multi-layered...

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...

With the Open Research Knowledge Graph (https://orkg.org) developed by TIB – Leibniz-Informationszentrum Technik und Naturwissenschaften and L3S Research...

Stanford / Winter 2021

With our new tool Stardog Explorer, it’s easier than ever to search, browse, and understand connected data. No querying, no code — just an intuitive interface that anyone can use.

This series summarizes a comprehensive taxonomy for machine learning on graphs and reports details on GraphEDM (Chami et. al), a new framework for unifying different learning approaches Graphs are…

Wireframing/interface design and user experience flow definition activities are a powerful tool to visualize knowledge graph applications.

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

Algorithms have been fundamental to recent global technological advances and, in particular, they have been the cornerstone of technical advances in one field rapidly being applied to another. We...