In the previous SPHERE news article focusing on the application of ontologies for digital twins of buildings, the importance of interoperability between data environments was stressed to reach a rich digital representation. In our philosophy, applications in the SPHERE ecosystem should always be replaceable to avoid vendor-lock in and to stimulate a best-of-breed approach. Such ... Read more

Until very recently, the objects of common use such as wearable devices, cars, watches, refrigerators and health-monitoring devices, did not produce or handle d

This article is an introduction into a field of graph-based code analysis. We will discuss a base concept of graph-based code analysis and learn how to build a Codebase Knowledge Graph (or Code Knowledge Graph or simply CKG) for a .NET Core project using Strazh.

Fixing the talent pipeline so that finding rocket scientists doesn't have to be rocket science

Learn how to visualize your intellectual property protection holistically to discover the gaps and evaluate their effects on dependent products and revenues.

Our latest benchmark report, Trillion Edge Knowledge Graph, is the first demonstration of a massive knowledge graph that consists of materialized data and Virtual Graphs spanning hybrid multicloud data sources. We prove it is possible to have a 1 trillion-edge knowledge graph and deliver sub-second query times while achieving a 98% cost savings.

A collection of important graph embedding, classification and representation learning papers with implementations. - benedekrozemberczki/awesome-graph-classification

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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 […]

Stanford / Winter 2021

Knowledge graphs bring vital changes to connected inventories for financials and enable them to save much time and expenses

I recently purchased a new laptop one with enough speed and space to let me do any number of projects that my nearly four-year-old workhorse was just not equipped to handle.

How simple structured data trumps clever machine learning

On the role of knowledge that is structured, explicit and intelligible in providing a path toward higher machine intelligence

I worked on some experiments on RDF-to-text generation. The goal is to generate coherent multi-sentence texts from data in a knowledge graph. While not...

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

Tracing the triumphs and challenges of two decades of Semantic Web research and applications.

Basic Introduction to what Graph Analytics is…

Read about how knowledge graphs can unlock meaning and thus create a smart future-proof living repository of scientific data and its relationships.

This blog highlights some of the new automation features that Cambridge Semantics is building into our scalable knowledge graph platform - Anzo.

Although artificial intelligence capabilities are improving daily, it is not always easy to put the AI rubber on the road – especially when it comes to understanding AI’s contextual data and problem-solving approaches. How about bringing in some “real” intelligence? Graphs are a typically human way

Multi-Model Database Use Case

We kicked off our #NeurIPS2020 series joined by @TacoCohen, ML Researcher at @Qualcomm @Qualcomm_Tech, to discuss his current research in equivariant networks and video compression using generative models, as well as his paper “Natural Graph Networks.”— The TWIML AI Podcast (@twimlai) December 22, 2020

Modelling complex relationships in RDF graphs

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