GraphNews

Qatalyst quantum accelerator software is in the cloud, ready-to-solve your complex constrained optimization problems, right now.

In this post, we would like to present our recent contribution in knowledge graphs embedding (KGE) models which was accepted at the AAAI…

"Directional Graph Networks" by Gabriele Corso, Saro Passaro, Dominique Beaini, Vincent Létourneau et al. Paper: https://lnkd.in/dpYeTiP #GraphNeuralNetworks...

Just published....

Announcing Stardog Explorer, a brand new way to easily explore the connections in your data. Powerful, intuitive new visualization and search capabilities make it easier for more types of users to benefit from your connected data in Stardog.

In recent years Knowledge Graphs have been used to solve one of the biggest problems not only in machine learning but also in computer…

The pandemic forced D&A leaders to step up research and analysis to respond effectively to change and uncertainty, the firm says.

Part of my Quick Take Series for quick limited-technical explanations on topics, in this case What is RDF? or Resource Description Framework, a major part of...

This articles introduces the Northwind sample database as a valuable resource to work on development of SPARQL feature proposals.

Topical Authority and Semantic SEO will be discussed more frequently with the concept of Structured Search Engine in the coming years. In this article, you will learn how to use these techniques to grow organic traffic.

Mike Tung: "I want to propose a much simpler statement of the benefits of knowledge technologies. KG technologies are indeed useful, but the most productive... 30 comments on LinkedIn

TerminusDB has raised $4.3 million to build what it calls the "knowledge collaboration infrastructure" for the internet.

Nice machine learning read of the week: E(n) Equivariant Graph Neural Networks. The paper: https://lnkd.in/dfZ3Gh4 Source code: https://lnkd.in/dNfzQRj...

As Enterprise Knowledge Graphs (EKGs) grow, support teams will see an increase in demand for documentation about your graph data.

Somesh Mohapatra strikes again with some representation learning for biopolymers, this time glycans! Alongside Joyce An, we propose a graph representation...

The field of Graph Neural Networks has matured substantially and here I propose to have a look at the top applications of GNNs.

Discover the new features in GDS 1.5, including new algorithms, more machine learning, and easier production deployment.

A provoking claim As a software anarchitect, I like to challenge the status quo: I propose to use RDF and SPARQL for the core domain logic of business applications. Many business applications consist of simple workflows to process rich information. My claim is that RDF and SPARQL are ideal to model and process such information while a workflow engine can orchestrate the processing steps. Cheap philosophy Algebraic data types are concrete structures capable of representing information explicitly and are becoming popular for domain modeling. But also a logical framework like RDF shines at rep...

Large amounts of threat intelligence information about mal-ware attacks are available in disparate, typically unstructured, formats. Knowledge graphs can capture this information and its context...

Cyber threat and attack intelligence information are available in non-standard format from heterogeneous sources. Comprehending them and utilizing them for threat intelligence extraction requires...

Recommender systems are an essential component of e-commerce marketplaces, helping consumers navigate massive amounts of inventory and find what they need or love. In this paper, we present an...

"OntoEnricher: A Deep Learning Approach for Ontology Enrichment from Unstructured Text" - Bidirectional LSTMs trained on a large #DBpedia and #Wikipedia corpuses to enrich information security ontologies.(Mohan et al, 2021)https://t.co/5ZTYWrDSnu pic.twitter.com/z5XeiaTAf0— WikiResearch (@WikiResearch) February 15, 2021

Demo of COMeT, a knowledge base construction engine that learns to produce new nodes and connections in commonsense knowledge graphs, on ATOMIC and ConceptNet.

"Digital Twins Definition Language is an open modeling language based on JSON-LD and RDF, by which developers can define the schema of the entities they expect to use in their graphs or topologies." This is an "open-source DTDL-based ontology .. for the real estate industry" https://t.co/icnwlMB5qy pic.twitter.com/at2kos9CDK— Aaron Bradley (@aaranged) February 16, 2021

Contextual advertising provides advertisers with the opportunity to target the context which is most relevant to their ads. However, its power cannot be fully utilized unless we can target the...

Visualization recommendation work has focused solely on scoring visualizations based on the underlying dataset and not the actual user and their past visualization feedback. These systems...

Knowledge graph embedding methods learn embeddings of entities and relations in a low dimensional space which can be used for various downstream machine learning tasks such as link prediction and...

This blog post will give you an overview of what we have developed in customer projects over the years as our game plan to build a Knowledge Graph-driven, FAIR Data platform and drive digital transformation with data. The post will show you how our product metaphactory can support you every step of the way, and will highlight examples from the life sciences and pharma domains.

Introducing Wiki Topic Grapher! 👾🐍🔥Leverage the power of Google #NLP to retrieve entity relationships from Wikipedia URLs or topics! + Get interactive graphs of connected entities+ Export results w/ ent. types+salience to CSV!▶️https://t.co/9M0zaMNIX8h/t @Streamlit 🧵 pic.twitter.com/ok9M3ypQgr— Charly Wargnier (@DataChaz) February 19, 2021