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 address the most pressing challenges of our times.
#10 - The Future of Data Management - Sean Martin
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 worlds, but how do we get there?
How do companies get to the state of looking through space and time and having unprecedented access to the wisdom hidden in Enterprise Data? What does the future IT Tech Stack look like? What decisions do CIOs have to make today to build a basis for long-term success?What completely new possibilities and business models arise? How will the business world change? What is Hype and what is Reality?
#09 - Cognitive Graph Analytics - Jans Aasman
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 Aasman, CEO of Franz Inc.
#08 - Graph Representation Learning - Guiseppe Futia
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 have to consider when it comes to Machine Learning based on ontologies?
#07 - Knowledge Graphs vs. Fake News - Daniel Schwabe
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 world. Can Knowledge Graphs help overcoming these problem by detecting Fake News or at least making the information evolution paths transparent? Thought provoking conversation with Daniel Schwabe.
#06 - Knowledge democratization & Abstract Wikipedia - Denny Vrandečić
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, Head of Special projects at Wikimedia, former Google Knowledge Graph ontologist and Founder of Croatian Wikipedia.
#05 - Ontologies, Knowledge & Human-Machine Interfaces - Panos Alexopoulos
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 would it take to build an Enterprise-wide Knowledge Graph?
Great conversation with Panos Alexopoulos, Head of Ontology @textkernel and Author of "Semantic Modelling for Data".