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Knowledge Graph Insights

Larry Swanson
Knowledge Graph Insights
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  • Mara Inglezakis Owens: A People-Loving Enterprise Architect – Episode 34
    Mara Inglezakis Owens Mara 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 that works for its users and consistently demonstrates its value to key stakeholders. We talked about: her role as an enterprise architect at a major US airline how her background as a humanities scholar, and especially as a rhetoric teacher, prepared her for her current work as a trusted business advisor some important mentoring she received early in her career how "digital anthropology" and product thinking fit into her enterprise architecture practice how she demonstrates the financial value of her work to executives and other stakeholders her thoughtful approach to the digitalization process and systems design the importance of documentation in knowledge engineering work how to sort out and document stakeholders' self-reports versus their actual behavior the scope of her knowledge modeling work, not just physical objects in the world, but also processes and procedures two important lessons she's learned over her career: don't be afraid to justify financial investment in your work, and "don't be so attached to an ideal outcome that you miss the best possible" Mara's bio Mara Inglezakis Owens is an enterprise architect who specializes in digitalization and knowledge management. She has deep experience in end-to-end supply chain as well as in planning, product, and program management. Mara’s background is in epistemology (history and philosophy of science, information science, and literature), which gives a unique, humanistic flavor to her practice. When she is not working, Mara enjoys aviation, creative writing, gardening, and raising her children. She lives in Minneapolis. Connect with Mara online LinkedIn email: mara dot inglezakis dot owens at gmail dot com Video Here’s the video version of our conversation: https://youtu.be/d8JUkq8bMIc Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 34. When think about architecting knowledge systems for a giant business like a global airline, you might picture huge databases and complex spaghetti diagrams of enterprise architectures. These do in fact exist, but the thing that actually makes these systems work is an understanding of the needs of the people who use, manage, and finance them. That's the important, human-focused work that Mara Inglezakis Owens does as an enterprise architect at a major US airline. Interview transcript Larry: Hi, everyone. Welcome to episode 34 of the Knowledge Graph Insights Podcast. I am really delighted today to welcome to the show, Mara, I'm going to get this right, Inglezakis Owens. She's an enterprise architect at a major US airline. So, welcome, Mara. Tell the folks a little bit more about what you're up to these days. Mara: Hi, everybody. My name's Mara. And these days I am achieving my childhood dream of working in aviation, not as a pilot, but that'll happen, but as an enterprise architect. I've been doing EA, also data and information architecture, across the whole scope of supply chain for about 10 years, everything from commodity sourcing to SaaS, software as a service, to now logistics. And a lot of my days, I spend interviewing subject matter experts, convincing business leaders they should do stuff, and on my best days, I get to crawl around on my hands and knees in an airplane hangar. Larry: Oh, fun. That is ... Yeah. I didn't know ... I knew that there's that great picture of you sitting in the jet engine, but I didn't realize this was the fulfillment of a childhood dream. That's awesome. But everything you've just said ties in so well to the tagline on your LinkedIn pro...
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  • Frank van Harmelen: Hybrid Human-Machine Intelligence for the AI Age – Episode 33
    Frank van Harmelen Much 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 support people. While some in the AI world seek to replace humans with machines, Frank focuses on AI systems that collaborate effectively with people. We talked about: his role as a professor of AI at the Vrije Universiteit in Amsterdam how rapid change in the AI world has affected the 10-year, €20-million Hybrid Intelligence Centre research he oversees the focus of his research on the hybrid combination of human and machine intelligence how the introduction of conversational interfaces has advance AI-human collaboration a few of the benefits of hybrid human-AI collaboration the importance of a shared worldview in any collaborative effort the role of the psychological concept of "theory of mind" in hybrid human-AI systems the emergence of neuro-symbolic solutions how he helps his students see the differences between systems 1 and 2 thinking and its relevance in AI systems his role in establishing the foundations of the semantic web the challenges of running a program that spans seven universities and employs dozens of faculty and PhD students some examples of use cases for hybrid AI-human systems his take on agentic AI, and the importance of humans in agent systems some classic research on multi-agent computer systems the four research challenges - collaboration, adaptation, responsibility, and explainability - they are tackling in their hybrid intelligence research his take on the different approaches to AI in Europe, the US, and China the matrix structure he uses to allocate people and resources to three key research areas: problems, solutions, and evaluation his belief that "AI is there to collaborate with people and not to replace us" Frank's bio Since 2000 Frank van Harmelen has played a leading role in the development of the Semantic Web. He is a co-designer of the Web Ontology Language OWL, which has become a worldwide standard. He co-authored the first academic textbook of the field, and was one of the architects of Sesame, an RDF storage and retrieval engine, which is in wide academic and industrial use. This work received the 10-year impact award at the International Semantic Web Conference. Linked Open Data and Knowledge Graphs are important spin-offs from this work. Since 2020, Frank is is scientific director of the Hybrid Intelligence Centre, where 50 PhD students and as many faculty members from 7 Dutch universities investigate AI systems that collaborate with people instead of replacing them. The large scale of modern knowledge graphs that contain hundreds of millions of entities and relationships (made possible partly by the work of Van Harmelen and his team) opened the door to combine these symbolic knowledge representations with machine learning. Since 2018, Frank has pivoted his research group from purely symbolic Knowledge Representation to Neuro-Symbolic forms of AI. Connect with Frank online Hybrid Intelligence Centre Video Here’s the video version of our conversation: https://youtu.be/ox20_l67R7I Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 33. As the AI landscape has evolved over the past few years, hybrid architectures that combine LLMs, knowledge graphs, and other AI technology have become the norm. Frank van Harmelen argues that the ultimate hybrid system must also include humans. He's running a 10-year, €20 million research program in the Netherlands to explore exactly this. His Hybrid Intelligence Centre investigates AI systems that collaborate with people instead of replacing them. Interview transcript Larry: Hi,
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  • Denny Vrandečić: Connecting the World’s Knowledge with Abstract Wikipedia – Episode 32
    Denny Vrandečić As 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, but - inspired by the success of other contributor-driven Wikimedia Foundation projects - Denny is confident that community can make it happen We talked about: his work as Head of Special Projects at the Wikimedia Foundation and his current projects: Wikifunctions and Abstract Wikipedia the origin story of his first project at Wikimedia - Wikidata a precursor project that informed Wikidata - Semantic MediaWiki the resounding success of the Wikidata project, the most edited wiki in the world, with half a million contributors how the need for more expressivity than Wikidata offers led to the idea for Abstract Wikipedia an overview of the Abstract Wikipedia project the abstract language-independent notation that underlies Abstract Wikipedia how Abstract Wikipedia will permit almost instant updating of Wikipedia pages with the facts it provides the capability of Abstract Wikipedia to permit both editing and use of knowledge in an author's native language their exploration of using LLMs to use natural language to create structured representations of knowledge how the design of Abstract Wikipedia encourages and facilitates contributions to the project the Wikifunctions project, a necessary precondition to Abstract Wikipedia the role of Wikidata as the Rosetta Stone of the web some background on the Wikifunctions project the community outreach work that Wikimedia Foundation does and the role of the community in the development of Abstract Wikipedia and Wikifunctions the technical foundations for his how to contribute to Wikimedia Foundation projects his goal to remove language barriers to allow all people to work together in a shared knowledge space a reminder that Tim Berners-Lee's original web browser included an editing function Denny's bio Denny Vrandečić is Head of Special Projects at the Wikimedia Foundation, leading the development of Wikifunctions and Abstract Wikipedia. He is the founder of Wikidata, co-creator of Semantic MediaWiki, and former elected member of the Wikimedia Foundation Board of Trustees. He worked for Google on the Google Knowledge Graph. He has a PhD in Semantic Web and Knowledge Representation from the Karlsruhe Institute of Technology. Connect with Denny online user Denny at Wikimedia Wikidata profile Mastodon LinkedIn email: denny at wikimedia dot org Resources mentioned in this interview Wikimedia Foundation Wikidata Semantic MediaWiki Wikidata: The Making Of Wikifunctions Abstract Wikipedia Meta-Wiki Video Here’s the video version of our conversation: https://youtu.be/iB6luu0w_Jk Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 32. The original plan for the World Wide Web was that it would be a two-way street, with opportunities to both discover and share knowledge. That promise was lost early on - and then restored a few years later when Wikipedia added an "edit" button to the internet. Denny Vrandečić is working to make that edit function even more powerful with Abstract Wikipedia, an innovative platform that lets web citizens both create and consume the world's knowledge, in their own language. Interview transcript Larry: Hi, everyone. Welcome to episode number 32 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Denny Vrandecic. Denny is best known as the founder of Wikidata, which we'll talk about more in just a minute. He's currently the Head of Special Projects at the Wikimedia Foundation. He's also a visiting professor at King's College Lo...
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  • Charles Ivie: The Rousing Success of the Semantic Web “Failure” – Episode 31
    Charles Ivie Since 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 knowledge graphs in enterprises and media companies is only growing as enterprise AI architectures mature. We talked about: his long work history in the knowledge graph world his observation that the semantic web is "the most catastrophically successful thing which people have called a failure" some of the measures of the success of the semantic web: ubiquitous RDF annotations in web pages, numerous knowledge graph deployments in big enterprises and media companies, etc. the long history of knowledge representation the role of RDF as a Rosetta Stone between human knowledge and computing capabilities how the abstraction that RDF permits helps connect different views of knowledge within a domain the need to scope any ontology in a specific domain the role of upper ontologies his transition from computer science and software engineering to semantic web technologies the fundamental role of knowledge representation tech - to help humans communicate information, to innovate, and to solve problems how semantic modeling's focus on humans working things out leads to better solutions than tech-driven approaches his desire to start a conversation around the fundamental upper principles of ontology design and semantic modeling, and his hypothesis that it might look something like a network of taxonomies Charles' bio Charles Ivie is a Senior Graph Architect with the Amazon Neptune team at Amazon Web Services (AWS). With over 15 years of experience in the knowledge graph community, he has been instrumental in designing, leading, and implementing graph solutions across various industries. Connect with Charles online LinkedIn Video Here’s the video version of our conversation: https://youtu.be/1ANaFs-4hE4 Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 31. Since the concept of the semantic web was introduced almost 25 years ago, many have dismissed it as a failure. Charles Ivie points out that it's actually been a rousing success. From the ubiquitous presence of RDF annotations in web pages to the mass adoption of knowledge graphs in enterprises and media companies, the semantic web has been here all along and only continues to grow as more companies discover the benefits of knowledge-representation technology. Interview transcript Larry: Hi everyone. Welcome to episode number 31 of the Knowledge Graph Insights Podcast. I am really happy today to welcome to the show Charles Ivie. Charles is currently a senior graph architect at Amazon's Neptune department. He's been in the graph community for years, worked at the BBC, ran his own consultancies, worked at places like The Telegraph and The Financial Times and places you've heard of. So welcome Charles. Tell the folks a little bit more about what you're up to these days. Charles: Sure. Thanks. Thanks, Larry. Very grateful to be invited on, so thank you for that. And what have I been up to? Yeah, I've been about in the graph industry for about 14 years or something like that now. And these days I am working with the Amazon Neptune team doing everything I can to help people become more successful with their graph implementations and with their projects. And I like to talk at conferences and join things like this and write as much as I can. And occasionally they let me loose on some code too. So that's kind of what I'm up to these days. Larry: Nice. Because you have a background as a software engineer and we will talk more about that later because I think that's really relevant to a lot of what we'll talk about.
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  • Andrea Gioia: Human-Centered Modeling for Data Products – Episode 30
    Andrea Gioia In 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 that might also be called a knowledge product. We talked about: his work as CTO at Quantyca, a data and metadata management consultancy his description of data products and their lifecycle how the lack of reusability in most data products inspired his current approach to modular, composable data products - and brought him into the world of ontology how focusing on specific data assets facilitates the creation of reusable data products his take on the role of data as a valuable enterprise asset how he accounts for technical metadata and conceptual metadata in his modeling work his preference for a federated model in the development of enterprise ontologies the evolution of his data architecture thinking from a central-governance model to a federated model the importance of including the right variety business stakeholders in the design of the ontology for a knowledge product his observation that semantic model is mostly about people, and working with them to come to agreements about how they each see their domain Andrea's bio Andrea Gioia is a Partner and CTO at Quantyca, a consulting company specializing in data management. He is also a co-founder of blindata.io, a SaaS platform focused on data governance and compliance. With over two decades of experience in the field, Andrea has led cross-functional teams in the successful execution of complex data projects across diverse market sectors, ranging from banking and utilities to retail and industry. In his current role as CTO at Quantyca, Andrea primarily focuses on advisory, helping clients define and execute their data strategy with a strong emphasis on organizational and change management issues. Actively involved in the data community, Andrea is a regular speaker, writer, and author of 'Managing Data as a Product'. Currently, he is the main organizer of the Data Engineering Italian Meetup and leads the Open Data Mesh Initiative. Within this initiative, Andrea has published the data product descriptor open specification and is guiding the development of the open-source ODM Platform to support the automation of the data product lifecycle. Andrea is an active member of DAMA and, since 2023, has been part of the scientific committee of the DAMA Italian Chapter. Connect with Andrea online LinkedIn (#TheDataJoy) Github Video Here’s the video version of our conversation: https://www.youtube.com/watch?v=g34K_kJGZMc Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 30. In the world of enterprise architectures, data products are emerging as a solution to the problem of siloed data and knowledge. As a data and metadata management consultant, Andrea Gioia helps his clients realize the value in their data assets by assembling them into composable, reusable data products. Built around collaboratively developed ontologies, these data products evolve into something that might also be called a knowledge product. Interview transcript Larry: Hi, everyone. Welcome to episode number 30 of the Knowledge Graph Insights podcast. I'm really happy today to welcome to the show Andrea Gioia. Andrea's, he does a lot of stuff. He's a busy guy. He's a partner and the chief technical officer at Quantyca, a consulting firm that works on data and metadata management. He's the founder of Blindata, a SaaS product that goes with his consultancy. I let him talk a little bit more about that. He's the author of the book Managing Data as a Product, and he's also, he comes out of the data heritage but he's now one of these knowledge people like us.
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Interviews with experts on semantic technology, ontology design and engineering, linked data, and the semantic web.
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