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Knowledge Graph Insights
Larry Swanson
10 episodes
21 hours ago
Interviews with experts on semantic technology, ontology design and engineering, linked data, and the semantic web.
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All content for Knowledge Graph Insights is the property of Larry Swanson and is served directly from their servers with no modification, redirects, or rehosting. The podcast is not affiliated with or endorsed by Podjoint in any way.
Interviews with experts on semantic technology, ontology design and engineering, linked data, and the semantic web.
Show more...
Technology
Business,
News,
Management,
Tech News
Episodes (10/10)
Knowledge Graph Insights
Torrey Podmajersky: Aligning Language and Meaning in Complex Systems – Episode 39
Torrey Podmajersky

Torrey Podmajersky is uniquely well-prepared to help digital teams align on language and meaning.

Her father's interest in philosophy led her to an early intellectual journey into semantics, and her work as a UX writer at companies like Google and Microsoft has attuned her to the need to discover and convey precise meaning in complex digital experiences.

This helps her span the "semantic gaps" that emerge when diverse groups of stakeholders use different language to describe similar things.

We talked about:

her work as president at her consultancy, Catbird Content, and as the author of two UX books
how her father's interest in philosophy and semantics led her to believe that everyone routinely thinks about what things mean and how to represent meaning
the role of community and collaboration in crafting the language that conveys meaning
how the educational concept of "prelecting" facilitates crafting shared-meaning experiences
the importance of understanding how to discern and account for implicit knowledge in experience design
how she identifies "semantic gaps" in the language that various stakeholders use
her discovery, and immediate fascination with, the Cyc project and its impact on her semantic design work
her take on the fundamental differences between how humans and LLMs create content

Torrey's bio
Torrey Podmajersky helps teams solve business and customer problems using UX and content at Google, OfferUp, Microsoft, and clients of Catbird Content. She wrote Strategic Writing for UX, is co-authoring UX Skills for Business Strategy, hosts the Button Conference, and teaches content, UX, and other topics at schools and conferences in North America and Europe.
Connect with Torrey online

LinkedIn
Catbird Content (newsletter sign-up)

Torrey's Books

Strategic Writing for UX
UX Skills for Business Strategy

Resources mentioned in this interview

Cyc project
Button Conference
UX Methods.org

Video
Here’s the video version of our conversation:

https://youtu.be/0GLpW9gAsG0
Podcast intro transcript
This is the Knowledge Graph Insights podcast, episode number 39. Finding the right language to describe how groups of people agree on the meaning of the things they're working with is hard. Torrey Podmajersky is uniquely well-prepared to meet this challenge. She was raised in a home where where it was common to have philosophical discussions about semantics over dinner. More recently, she's worked as a designer at tech companies like Google, collaborating with diverse teams to find and share the meaning in complex systems.
Interview transcript
Larry:
Hi everyone. Welcome to episode number 39 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Torrey Podmajersky. I've known Torrey for years from the content world, the UX design and content design and UX writing and all those worlds. I used to live very closer to her office in Seattle, but Torrey's currently the president at Catbird Content, her consultancy, and she's guest faculty at the University of Washington iSchool. She does all kinds of interesting stuff, very accomplished author. So welcome Torrey. Tell the folks a little bit more about what you're up to and where all the books are at these days.

Torrey:
Thanks so much, Larry. I am up to my neck in finishing the books right now. So one just came out the second edition of Strategic Writing for UX that has a brand new chapter on building LLMs into products and updates throughout, of course since it came out six years ago. But I'm also working on the final manuscript with twoTorrey Podmajersky co-authors f...
Show more...
3 weeks ago
32 minutes 40 seconds

Knowledge Graph Insights
Casey Hart: The Philosophical Foundations of Ontology Practice – Episode 38
Casey Hart

Ontology 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 in general.

We talked about:

his work as a lead ontologist at Ford and as an ontology consultant
his academic background in philosophy
the variety of pathways into ontology practice
the philosophical principles like metaphysics, epistemology, and logic that inform the practice of ontology
his history with the the Cyc project and employment at Cycorp
how he re-uses classes like "category" and similar concepts from upper ontologies like gist
his definition of "AI" - including his assertion that we should use term to talk about a practice, not a particular technology
his reminder that ontologies are models and like all models can oversimplify reality

Casey's bio
Casey Hart is the lead ontologist for Ford, runs an ontology consultancy, and pilots a growing YouTube channel. He is enthusiastic about philosophy and ontology evangelism. After earning his PhD in philosophy from the University of Wisconsin-Madison (specializing in epistemology and the philosophy of science), he found himself in the private sector at Cycorp. Along his professional career, he has worked in several domains: healthcare, oil & gas, automotive, climate science, agriculture, and retail, among others. Casey believes strongly that ontology should be fun, accessible, resemble what is being modelled, and just as complex as it needs to be.

He lives in the Pacific Northwest with his wife and three daughters and a few farm animals.
Connect with Casey online

LinkedIn
ontologyexplained at gmail dot com
Ontology Explained YouTube channel

Video
Here’s the video version of our conversation:

https://youtu.be/siqwNncPPBw

Podcast intro transcript
This is the Knowledge Graph Insights podcast, episode number 38. When the subject of philosophy comes up in relation to ontology practice, it's typically cited as the origin of the term, and then the subject is dropped. Casey Hart sees many other connections between ontology practice and it its philosophical roots. In addition to logic as the foundation of OWL, he shows how philosophy concepts like epistemology, metaphysics, and rhetoric are relevant to both knowledge graphs and AI technology in general.
Interview transcript

Larry:
Hi, everyone. Welcome to episode number 38 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Casey Hart. Casey has a really cool YouTube channel on the philosophy behind ontology engineering and ontology practice. Casey is currently an ontologist at Ford, the motor car company. So welcome Casey, tell the folks a little bit more about what you're up to these days.

Casey:
Hi. Thanks, Larry. I'm super excited to be here. I've listened to the podcast, and man, your intro sounds so smooth. I was like, "I wonder how many edits that takes." No, you just fire them off, that's beautiful.

Casey:
Yeah, so like you said, these days I'm the ontologist at Ford, so building out data models for sensor data and vehicle information, all those sorts of fun things. I am also working as a consultant. I've got a couple of different startup healthcare companies and some cybersecurity stuff, little things around the edge. I love evangelizing ontology, talking about it and thinking about it. And as you mentioned for the YouTube channel, that's been my creative outlet.
Show more...
2 months ago
39 minutes 6 seconds

Knowledge Graph Insights
Chris Mungall: Collaborative Knowledge Graphs in the Life Sciences – Episode 37
Chris Mungall

Capturing 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 collaborative methods and AI-augmented computational tooling that streamlines the organization of these precious scientific discoveries.

We talked about:

his biosciences and genetics work at the Berkeley Lab
how the complexity and the volume of biological data he works with led to his use of knowledge graphs
his early background in AI
his contributions to the gene ontology
the unique role of bio-curators, non-semantic-tech biologists, in the biological ontology community
the diverse range of collaborators involved in building knowledge graphs in the life sciences
the variety of collaborative working styles that groups of bio-creators and ontologists have created
some key lessons learned in his long history of working on large-scale, collaborative ontologies, key among them, meeting people where they are
some of the facilitation methods used in his work, tools like GitHub, for example
his group's decision early on to commit to version tracking, making change-tracking an entity in their technical infrastructure
how he surfaces and manages the tacit assumptions that diverse collaborators bring to ontology projects
how he's using AI and agentic technology in his ontology practice
how their decision to adopt versioning early on has enabled them to more easily develop benchmarks and evaluations
some of the successes he's had using AI in his knowledge graph work, for example, code refactoring, provenance tracking, and repairing broken links

Chris's bio
Chris Mungall is Department Head of Biosystems Data Science at Lawrence Berkeley National Laboratory. His research interests center around the capture, computational integration, and dissemination of biological research data, and the development of methods for using this data to elucidate biological mechanisms underpinning the health of humans and of the planet. He is particularly interested in developing and applying knowledge-based AI methods, particularly Knowledge Graphs (KGs) as an approach for integrating and reasoning over multiple types of data. Dr. Mungall and his team have led the creation of key biological ontologies for the integration of resources covering gene function, anatomy, phenotypes and the environment. He is a principal investigator on major projects such as the Gene Ontology (GO) Consortium, the Monarch Initiative, the NCATS Biomedical Data Translator, and the National Microbiome Data Collaborative project.
Connect with Chris online

LinkedIn
Berkeley Lab

Video
Here’s the video version of our conversation:

https://youtu.be/HMXKFQgjo5E
Podcast intro transcript
This is the Knowledge Graph Insights podcast, episode number 37. The span of the life sciences extends from the atomic level up to planetary ecosystems. Combine this scale and complexity with the variety of collaborators who manage information about the field, and you end up with a huge knowledge-management challenge. Chris Mungall and his colleagues have developed collaborative methods and computational tooling that enable the construction of ontologies and knowledge graphs that capture this crucial scientific knowledge.
Interview transcript
Larry:
Hi everyone. Welcome to episode number 37 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Chris Mungall. Chris is a computational scientist working in the biosciences at the Lawrence ...
Show more...
3 months ago
32 minutes 52 seconds

Knowledge Graph Insights
Emeka Okoye: Exploring the Semantic Web with the Model Context Protocol – Episode 36
Emeka Okoye

Semantic 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 MCP service that lets any developer surf the semantic web without having to learn its specialized language.

We talked about:

his long history in knowledge engineering and AI agents
his deep involvement in the business and technology communities in Nigeria, including founding the country's first internet startup
how he was building knowledge graphs before Google coined the term
an overview of MCP, the Model Context Protocol, and its benefits
the RDF Explorer MCP server he has developed
how the MCP protocol and helps ease some of the challenges that semantic web developers have traditionally faced
the capabilities of his RDF Explorer:

facilitating communication between AI applications, language models, and RDF data
enabling graph exploration and graph data analysis via SPARQL queries
browsing, accessing, and evaluating linked-open-data RDF resources


the origins of RDF Explorer in his attempt to improve ontology engineering tooling
his objections to "vibe ontology" creation
the ability of RDF Explorer to let non-RDF developers users access knowledge graph data
how accessing knowledge graph data addresses the problem of the static nature of the data in language models
the natural connections he sees between neural network AI and symbolic AI like knowledge graphs, and the tech tribalism he sees in the broader AI world that prevents others from seeing them
how the ability of LLMs to predict likely language isn't true intelligence or actual knowledge
some of the lessons he learned by building the RDF Explorer, e.g., how the MCP protocol removes a lot of the complexity in building hybrid AI solutions
how MCP helps him validate the ontologies he creates

Emeka's bio
Emeka is a Knowledge Engineer, Semantic Architect, and Generative AI Engineer who leverages his over two decades of expertise in ontology and knowledge engineering and software development to architect, develop, and deploy innovative, data-centric AI products and intelligent cognitive systems to enable organizations in their Digital Transformation journey to enhance their data infrastructure, harness their data assets for high-level cognitive tasks and decision-making processes, and drive innovation and efficiency enroute to achieving their organizational goals.

Emeka’s experience has embraced a breadth of technologies his primary focus being solution design, engineering and product development while working with a cross section of professionals across various cultures in Africa and Europe in solving problems at a complex level. Emeka can understand and explain technologies from deep diving under the hood to the value proposition level.
Connect with Emeka online

LinkedIn
Making Knowledge Graphs Accessible: My Journey with MCP and RDF Explorer
RDF Explorer (GitHub)

Video
Here’s the video version of our conversation:

https://youtu.be/GK4cqtgYRfA
Podcast intro transcript
This is the Knowledge Graph Insights podcast, episode number 36. The widespread adoption of semantic technologies has created a variety of linked data resources on the web. Until recently, you had to learn semantic tools to access that data. The arrival of LLMs, with their conversational interfaces and ability to translate natural language into knowledge graph queries,
Show more...
3 months ago
34 minutes 51 seconds

Knowledge Graph Insights
Tom Plasterer: The Origins of FAIR Data Practices – Episode 35
Tom Plasterer

Shortly 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 web community, scientific research, and the pharmaceutical industry, the FAIR data idea has spread across academia, research, industry, and enterprises of all kinds.

We talked about:

his recent move from a big pharma company to Exponential Data where he leads the knowledge graph and FAIR data practices
the direct line from the original semantic web concept to FAIR data principles
the scope of the FAIR acronym, not just four concepts, but actually 15
how the accessibility requirement in FAIR distinguishes the standard from the open data
the role of knowledge graphs in the implementation of a FAIR data program
the intentional omission of prescribed implementations in the development of FAIR and the ensuing variety of implementation patterns
how the desire for consensus in the biology community smoothed the development of the FAIR standard
the role of knowledge graphs in providing a structure for sharing terminology and other information in a scientific community
how his interest in omics led him to computer science and then to the people skills crucial to knowledge graph work
the origins of the impetus for FAIR in European scientific research and the pharmaceutical industry
the growing adoption of FAIR as enterprises mature their web thinking and vendors offer products to help with implementations
the roles of both open science and the accessibility needs in industry contributed to the development of FAIR
the interesting new space at the intersection of generative AI and FAIR and knowledge graph
the crucial foundational role of FAIR in AI systems

Tom's bio
Dr. Tom Plasterer is a leading expert in data strategy and bioinformatics, specializing in the application of knowledge graphs and FAIR data principles within life sciences and healthcare. With over two decades of experience in both industry and academia, he has significantly contributed to bioinformatics, systems biology, biomarker discovery, and data stewardship. His entrepreneurial ventures include co-founding PanGenX, a Personalized Medicine/Pharmacogenetics Knowledge Base start-up, and directing Project Planning and Data Interpretation at BG Medicine. During his extensive tenure at AstraZeneca, he was instrumental in championing Data Centricity, FAIR Data, and Knowledge Graph initiatives across various IT and scientific business units.

Currently, Dr. Plasterer serves as the Managing Director of Knowledge Graph and FAIR Data Capability at XponentL Data, where he defines strategy and implements advanced applications of FAIR data, knowledge graphs, and generative AI for the life science and healthcare industries. He is also a prominent figure in the community, having co-founded the Pistoia Alliance FAIR Data Implementation group and serving on its FAIR data advisory board. Additionally, he co-organizes the Health Care and Life Sciences symposium at the Knowledge Graph Conference and is a member of Elsevier’s Corporate Advisory Board.
Connect with Tom online

LinkedIn

Video
Here’s the video version of our conversation:

https://youtu.be/Lt9Dc0Jvr4c
Podcast intro transcript
This is the Knowledge Graph Insights podcast, episode number 35. With the introduction of semantic web technologies in the early 2000s, the World Wide Web began to look something like a giant database. And with great data, comes great responsibility.
Show more...
4 months ago
32 minutes 13 seconds

Knowledge Graph Insights
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:
Show more...
4 months ago
30 minutes 57 seconds

Knowledge Graph Insights
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...
Show more...
5 months ago
29 minutes 31 seconds

Knowledge Graph Insights
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.
Show more...
6 months ago
32 minutes 34 seconds

Knowledge Graph Insights
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.
Show more...
6 months ago
33 minutes 49 seconds

Knowledge Graph Insights
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,
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6 months ago
32 minutes 48 seconds

Knowledge Graph Insights
Interviews with experts on semantic technology, ontology design and engineering, linked data, and the semantic web.