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From data to knowledge and AI via graphs: Technology to support a knowledge-based economy

ZDNet

These past few months have not been kind to any of us. The ripples caused by the COVID-19 crisis are felt far and wide, and the world's economies have taken a staggering blow. As with most things in life, however, this crisis has also brought some interesting side effects. Reimagining business for the digital age is the number-one priority for many of today's top executives. We offer practical advice and examples of how to do it right.


A Standard to build Knowledge Graphs: 12 Facts about SKOS

@machinelearnbot

These days, many organisations have begun to develop their own knowledge graphs. One reason might be to build a solid basis for various machine learning and cognitive computing efforts. For many of those, it remains still unclear where to start. SKOS offers a simple way to start and opens many doors to extend a knowledge graph over time. The usage of open standards for data and knowledge models eliminates proprietary vendor lock-in.


A Standard to build Knowledge Graphs: 12 Facts about SKOS

@machinelearnbot

These days, many organisations have begun to develop their own knowledge graphs. One reason might be to build a solid basis for various machine learning and cognitive computing efforts. For many of those, it remains still unclear where to start. SKOS offers a simple way to start and opens many doors to extend a knowledge graph over time. The usage of open standards for data and knowledge models eliminates proprietary vendor lock-in.


Multi-Modal Knowledge Graph Construction and Application: A Survey

arXiv.org Artificial Intelligence

Recent years have witnessed the resurgence of knowledge engineering which is featured by the fast growth of knowledge graphs. However, most of existing knowledge graphs are represented with pure symbols, which hurts the machine's capability to understand the real world. The multi-modalization of knowledge graphs is an inevitable key step towards the realization of human-level machine intelligence. The results of this endeavor are Multi-modal Knowledge Graphs (MMKGs). In this survey on MMKGs constructed by texts and images, we first give definitions of MMKGs, followed with the preliminaries on multi-modal tasks and techniques. We then systematically review the challenges, progresses and opportunities on the construction and application of MMKGs respectively, with detailed analyses of the strength and weakness of different solutions. We finalize this survey with open research problems relevant to MMKGs.


Knowledge Graphs: Data in Context for Responsive Businesses [New Book]

#artificialintelligence

Knowledge graphs have been around for almost half a century – as the term was first coined in 1972! For a long time, they simply languished in the academic world until Google announced their knowledge graph in 2012. Since then, knowledge graphs have evolved quite dramatically, and now there is no turning back. The last 10 years have seen a meteoric rise in machine learning (ML) and artificial intelligence (AI). Because of their ability to drive intelligence into data and add context, knowledge graphs are used to make ML and AI more reliable, robust, trustworthy, and explainable.