What’s the best way to get to know your customers? Learn how Wunderman Thompson understands their market using their identity graph.

At the first-ever Disney Platform Tech Showcase, Disney Advertising Sales highlighted the Company’s investments in technology and innovation – unveiling strategies and solutions around data-driven precision, premium ad experiences and frictionless transactions for marketers.

A set of benchmark repositories for node classification on knowledge graphs (work in progress).

Seeking to develop a multilingual Wikipedia where content can be shared among language editions.

Introducing a completely redesigned release with a unique user experience

There is a great deal of talk these days about using knowledge graphs to help manage large amounts of data. Learn the basics of knowledge graphs.

"The UI allows individuals with no previous knowledge of the Semantic Web to query the DBpedia knowledge base...." > Interface to Query and Visualise Definitions from a Knowledge Base @anelia12430996 & Hélène De Ribaupierre https://t.co/QGSJSEq4Ab pic.twitter.com/EIhZRVikK0— Aaron Bradley (@aaranged) March 15, 2021

Need to link #KnowledgeGraphs? Our article on the #OpenSource link discovery framework LIMES (https://t.co/zSp8nSaDNG) is now available at https://t.co/Fy2JMNRKQE. With LIMES, we support #OpenScience on the integration of knowlege graphs @MAhmedSherif @kvndrsslr @mommi84— Axel Ngonga (@NgongaAxel) March 18, 2021

NetVec: A Scalable Hypergraph Embedding System https://t.co/CHaT76MA7T pic.twitter.com/ZVChTtb0zH— Aaron Bradley (@aaranged) March 18, 2021

This demo allows hands-on experience on Knowledge Graph databases by executing SPARQL queries side-by-side with their SQL counterparts.

What is Semantic SEO? SEO is becoming more Semantic, and it's worth looking at how it is moving in that direction with knowledge panels, question-answering, and related entities

Abstract

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…

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.

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.

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.

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.

Knowledge about entities and their interrelations is a crucial factor of success for tasks like question answering or text summarization. Publicly available knowledge graphs like Wikidata or...

Accurate cardinality estimates are a key ingredient to achieve optimal query plans. For RDF engines, specifically under common knowledge graph processing workloads, the lack of schema, correlated...

This paper introduces a new model to learn graph neural networks equivariant to rotations, translations, reflections and permutations called E(n)-Equivariant Graph Neural Networks (EGNNs). In...

The code property graph (CPG) is a data structure designed to mine large codebases for instances of programming patterns via a domain-specific query language. It was first introduced in the proceedings of the IEEE Security and Privacy conference in 2014 (publication, PDF) in the context of vulnerability discovery in C system code and the Linux kernel in particular.

Selected chapters from my book, A Knowledge Representation Practionary, are now available for free download.

Recent years have seen the growing adoption of non-relational data models for representing diverse, incomplete data. Among these, the RDF graph-based data model has seen ever-broadening adoption,...

Thu, Mar 25, 2021, 9:00 AM: The lack of a convenient way to capture annotations and statements about individual RDF triples has been a long standing issue for RDF. Such annotations are a native featur