#05 - Ontologies, Knowledge & Human-Machine Interfaces - Panos Alexopoulos

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?

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.

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?

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.

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.

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".

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 what if we could build a Science Knowledge Graph that contains all scientific knowledge and one day will be able to reason, retrieve relevant information, detect scientific gaps and deduce new knowledge? How would such a Knowledge Graph look like and how would we use it? Can we even reach such a deep manifestation of humanity’s collective intelligence?Interview with Prog. Sören Auer, Director & Head of Research at TIB, University of Hannover and pioneer in the semantic web movement.

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 generalisation, explainability and adaptability of AI systems and thus paves the way towards Artificial General Intelligence.Can commonsense Knowledge Graphs teach Neural Networks to generalise and explain? - Interview with developer of Knowledge-infused Learning Manas Gaur

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 would be much less enjoyable. But how can we build in these capabilities alongside common sense knowledge into machines? And why would that help?

Can we build a #google for enterprise data? How can #KnowledgeGraphs & #HybridAI help executives make better business decisions, and accelerate the evolution towards enterprise collective intelligence?“The direction is very clear and you can’t stop it” - Inspiring talk with Dan McCreary !