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Interviews with experts on semantic technology, ontology design and engineering, linked data, and the semantic web.
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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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Casey Hart: The Philosophical Foundations of Ontology Practice – Episode 38
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Casey HartOntology engineering has its roots in the idea of ontology as defined by classical philosophers.Casey Hart sees many other connections between professional ontology practice and the academic discipline of philosophy and shows how concepts like epistemology, metaphysics, and rhetoric are relevant to both knowledge graphs and AI technology …
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Chris Mungall: Collaborative Knowledge Graphs in the Life Sciences – Episode 37
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Chris MungallCapturing knowledge in the life sciences is a huge undertaking. The scope of the field extends from the atomic level up to planetary-scale ecosystems, and a wide variety of disciplines collaborate on the research.Chris Mungall and his colleagues at the Berkeley Lab tackle this knowledge-management challenge with well-honed collaborativ…
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Emeka Okoye: Exploring the Semantic Web with the Model Context Protocol – Episode 36
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Emeka OkoyeSemantic technologies permit powerful connections across a variety of linked data resources across the web. Until recently, developers had to learn the RDF language to discover and use these resources.Leveraging the new Model Context Protocol (MCP) and LLM-powered natural-language interfaces, Emeka Okoye has created the RDF Explorer, an …
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Tom Plasterer: The Origins of FAIR Data Practices – Episode 35
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Tom PlastererShortly after the semantic web was introduced, the demand for discoverable and shareable data arose in both research and industry.Tom Plasterer was instrumental in the early conception and creation of the FAIR data principle, the idea that data should be findable, accessible, interoperable, and reusable.From its origins in the semantic…
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Mara Inglezakis Owens: A People-Loving Enterprise Architect – Episode 34
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Mara Inglezakis OwensMara Inglezakis Owens brings a human-centered focus to her work as an enterprise architect at a major US airline.Drawing on her background in the humanities and her pragmatic approach to business, she has developed a practice that embodies both "digital anthropology" and product thinking.The result is a knowledge architecture t…
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Frank van Harmelen: Hybrid Human-Machine Intelligence for the AI Age – Episode 33
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Frank van HarmelenMuch of the conversation around AI architectures lately is about neuro-symbolic systems that combine neural-network learning tech like LLMs and symbolic AI like knowledge graphs.Frank van Harmelen's research has followed this path, but he puts all of his AI research in the larger context of how these technical systems can best sup…
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Denny Vrandečić: Connecting the World’s Knowledge with Abstract Wikipedia – Episode 32
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Denny VrandečićAs the founder of Wikidata, Denny Vrandečić has thought a lot about how to better connect the world's knowledge.His current project is Abstract Wikipedia, an initiative that aims to let anyone anywhere on the planet contribute to, and benefit from, the world's collective knowledge, in their native language.It's an ambitious goal, b…
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Charles Ivie: The Rousing Success of the Semantic Web “Failure” – Episode 31
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Charles IvieSince the semantic web was introduced almost 25 years ago, many have dismissed it as a failure.Charles Ivie shows that the RDF standard and the knowledge-representation technology built on it have actually been quite successful.More than half of the world's web pages now share semantic annotations and the widespread adoption of knowledg…
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Andrea Gioia: Human-Centered Modeling for Data Products – Episode 30
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Andrea GioiaIn recent years, data products have emerged as a solution to the enterprise problem of siloed data and knowledge.Andrea Gioia helps his clients build composable, reusable data products so they can capitalize on the value in their data assets.Built around collaboratively developed ontologies, these data products evolve into something tha…
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Dave McComb: Semantic Modeling for the Data-Centric Enterprise – Episode 29
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Dave McCombDuring the course of his 25-year consulting career, Dave McComb has discovered both a foundational problem in enterprise architectures and the solution to it.The problem lies in application-focused software engineering that results in an inefficient explosion of redundant solutions that draw on overlapping data sources.The solution that …
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#10 - The Future of Data Management - Sean Martin
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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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#06 - Knowledge democratization & Abstract Wikipedia - Denny Vrandečić
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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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#05 - Ontologies, Knowledge & Human-Machine Interfaces - Panos Alexopoulos
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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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#02 - Intelligence & NLU, the ultimate test for AI - Walid Saba
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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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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 !저자 Boris Shalumov
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