GraphNews

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To overcome such inherent challenges with graph neural networks and improve recommendation abilities, LinkedIn has created a process it calls Performance-Adaptive Sampling Strategy (PASS). that uses AI to select the neighbors in graphs that are the most relevant, thus improving predictive accuracy.

By Alex Hutter, Falguni Jhaveri and Senthil Sayeebaba

The Studio Search platform takes a portion of the federated graph and makes it searchable.

Knowledge graphs (KGs) are a novel paradigm for the representation, retrieval, and integration of data from highly heterogeneous sources. Within just a few years, KGs and their supporting technologie...

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This week, enterprise data catalog provider data.world announced both a $50 million Series C round and a new suite of data catalog management capabilities

Information extraction, drug discovery, and software analysis are just a few applications of this versatile tool.

The complexity of cancer research stems from leaning on several biomedical disciplines for relevant sources of data, many of which are complex in their own right. A holistic view of cancer—which is critical for precision medicine approaches—hinges on integrating a variety of heterogeneous data sources under a cohesive knowledge model, a role which biomedical ontologies can fill. This study reviews the application of ontologies and knowledge graphs in cancer research. In total, our review encompasses 141 published works, which we categorized under 14 hierarchical categories according to their usage of ontologies and knowledge graphs. We also review the most commonly used ontologies and newly developed ones. Our review highlights the growing traction of ontologies in biomedical research in general, and cancer research in particular. Ontologies enable data accessibility, interoperability and integration, support data analysis, facilitate data interpretation and data mining, and more recently, with the emergence of the knowledge graph paradigm, support the application of Artificial Intelligence methods to unlock new knowledge from a holistic view of the available large volumes of heterogeneous data.

Knowledge graphs, the technology powering Google, Facebook and Apple, is now unlocking value across the financial sector. Knowledge graphs are transforming critical capabilities and enterprises are increasingly looking to the technology to enhance their tax strategy, perform compliance and improve customer service.

A new generation of note-taking tools helps us quickly organize thoughts as knowledge graphs

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Automating language is not magic; it can take years of careful work to piece together what people mean in the context of corporate help desks, HR programs and the like. Such is the work of startup PeopleReign.

While the NLP field has been growing at an exponential rate for the last two years — thanks to the development of transfer based models — their applications have been limited in scope for the job…

Nature Machine Intelligence - The number of graph neural network papers in this journal has grown as the field matures. We take a closer look at some of the scientific applications.

500 million+ members | Manage your professional identity. Build and engage with your professional network. Access knowledge, insights and opportunities.

500 million+ members | Manage your professional identity. Build and engage with your professional network. Access knowledge, insights and opportunities.

Data on Native communities are not at the levels needed for accuracy in A.I.-driven tools. A group is trying to solve that problem.

Tributary is a library for constructing dataflow graphs in python! pip install tributary Don't forget to star the repository! https://lnkd.in/dxWXfw_Y...

Starting a new Inductive Link Prediction Challenge 2022

The video of Guandao Yang discussing his paper "Geometry processing with Neural Fields" is up! YouTube Link: https://lnkd.in/dGqu2vcN I am excited to ...

With the right data fabric, people and algorithms can make better decisions and reduce the risk of data misuse or misinterpretation.

Graph data linked with geographic data will provide new means of data discovery, developers were told at a recent conference hosted by Esri.

Some of the modern GNNs have their roots in methods originally developed in the signal processing domain.   Graph Signal Processing started with two directions...

In the last couple of months, I’ve been noticing a gradual shift in the kind of articles that we receive at Data Science Central. We still get a fair amount of data science content, but increasingly (and admittedly with a bit of encouragement) we’re seeing more articles centered around graphs and semantics. I don’t believe… Read More »DSC Weekly Digest 22 February 2022: Graphology

It is nearly certain that, if you have ever played a 3D video game, watched a CGI-effects-laden movie, or seen increasingly hyperrealistic imagery, you have encountered a scene graph without realizing it. Scene graphs are pervasive in everything from media to medicine, from augmented reality to industrial digital twins, and they are increasingly playing an… Read More »Scene Graphs and Semantics

The Semantic Web has garnered a reputation for complexity among both Javascript and Python developers, primarily because, well, it’s not JSON, and JSON has become the data language of the web. Why learn some obscure language when JSON is perfectly capable of describing everything, right? Well, sort of. The problem that JSON faces, is actually… Read More »Why JSON Users Should Learn Turtle