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In just a few years Knowledge Graphs have exploded in usage, as has their impact in the world of Artificial Intelligence. Semantic AI has become a significant part of text analytics, search engines, chat-bots and more. And yet, few people outside of niche tech communities are fully aware of how semantic knowledge graphs can be leveraged.In the Podcast "Chaos Orchestra" we will explore how Knowledge Graphs can be applied over the next decade to boost many areas of Artifical Intelligence and a ...
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Knowledge Graphs revolutionise the way companies make use of their data. The technology has the potential to turn every digitised piece of knowledge in a company into actionable insights. You can exceed even Google’s Search capabilities by creating an intelligent platform with knowledge graph. Many of us can imagine our idealistic future data dream…
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Can Knowledge Graphs help to build better Cognitive Models? How will Knowledge Graphs look like in the future and how will we interact with them? Why didn't Knowledge Graphs solve COVID-19-related data problems? How far away are Technocracy and Digital Immortality? Extrapolating from 40 years of Knowledge Graphs and cognitive models with Dr. Jans A…
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Graph Neural Networks are very effective in dealing with complex network data structures to perform label and link predictions. They can process typological and structural information from social networks to protein pathways. But can they also work with multi-dimensional and dynamic data models of Semantic Graphs? What information loss does one hav…
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We have never been closer to knowledge democratisation and collective intelligence. However, the enabling technology is a blessing and a curse at the same time. Fake News and Filter Bubbles dominate the spread of information in social networks and search engines, influencing our personal trust chains and constantly directing our perspective on the …
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Wikipedia, Google and social networks transformed the way of knoweldge aggregation and spread - but can we make all of humanty's knoweldge machine-readable? Are Knoweldge Graphs enough to achieve that? What technological and social challenges come with Knoweldge democratization? Inspiring and thought provoking conversation with Denny Vrandečić, Hea…
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Ontologies are a way to represent and communicate knowledge, understandable to both - machines and humans. But what level of expressivity is needed to be able to convey human thoughts and human understanding of the world to machines? Are current graph representation models sufficient for generalisation and reasoning? How many ontology engineers wou…
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It is nearly impossible for a scientist to process all relevant information to one's field of research. Due to “antique”, document-based knowledge transmission methods, scientists are deriving hypotheses from a smaller and smaller fraction of our collective knowledge. It seems that science has outgrown the human mind and its limited capacities. But…
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Deep Learning has proven to be the primary technique to address a number of problems. But each application of AI inevitably encounters unexpected scenarios (edge cases) in which the system does not perform as required. Knowledge-infused learning uses commonsense knowledge encoded in Knowledge Graphs in order to provide capabilities like generalisat…
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Despite huge investments into Deep Learning we did not get close to making machines understand natural language (NLU). Can semantic approaches make up for weaknesses of Deep Learning like for example abstraction and generalization ? If humans would need to touch hundreds of hot ovens before they being able to extrapolate and generalize - our lives …
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