Knowledge Graph Insights

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 for UX Skills for Business Strategy. That'll be a wine pairing guide, a deep reference book that connects the business impact that you might want to make, whether you're a UX pro or a PM or a knowledge graph enthusiast working somewhere in product and connecting it to the UX skills you might want to use to make those impacts.

Larry:

Excellent. I can't wait to read both of those. I love the first edition of the Strategic Writing for UX book, but... Hey, I want to talk today though about, this is the Knowledge Graph Insights podcast, and you recently did this great post and we'll talk more about it in detail in a bit about how you had discovered the Cyc project, which is a real pioneering project in the semantic technology field and really foundational to a lot of the knowledge graph stuff that's happening today. But I want to start with one of the other things we talked about before we went on the air was your observation of the kind of common philosophical roots that we have in rhetoric, maybe not necessarily rhetoric, but the stuff that we do as word nerds, as meaning nerds, as all these different kinds of technology nerds that we are. Tell me a little bit about what you meant because you just hinted that and I was like, oh, good philosophy. I love philosophy.

Torrey:

Yeah, I love philosophy too, especially through my dad. My dad was a philosophy major at Haverford College and it has deeply influenced his life and his work in semantic knowledge spaces. And I got to grow up in that context thinking that everybody thought deeply about what things meant and how we represent those meanings. I mean, the Plato's Allegory of the Cave was my bedtime story to the extent that we all knew Plato in the cave, geez, dad, just fine. Plato in the cave. We don't really know anything. All we have is facsimiles and representations of meaning and representations of reality, and through that we construct meaning. And I feel like that's all we're ever doing is using language to construct meaning based on our inability to fully perceive reality.

Larry:

And just for folks who aren't familiar, I love Plato's Allegory of the Cave. It's these poor people chained to a wall and behind them is a projector projecting stuff on the wall in front of them. So all they see is this projection of an imitation of reality, which is much like what we're doing with either both UX writing and I think ontology design and semantic engineering. So that's the perfect analogy to come into this. But your job for the last, I don't know, because you made the transition from teaching to Xbox, what? 10, 12 years ago or something like that?

Torrey:

In 2010, I joined Xbox and before that I had a short stint in internal communications in a division at Microsoft working for a VP there.

Larry:

But you've been in the word biz and the meaning biz for a long time because UX writing is, how did you say it? You have to convey meaning. That's the whole point of UX writing is to just get past random words to actually, what are we talking about here?

Torrey:

It's to make the words that people understand so quickly while they're in an experience, they're just trying to use it. They're not there to read. So we want the words to disappear into ephemeral meaning in their head that they don't even remember. They just knew what to do and which button to press and where to go next to get done what they wanted to get done.

Larry:

And one of the things about that is getting to that language to do that in an experience, that's a team sport. One of the other things that really struck me about that post you did was the role of community in language and meaning. Talk a little bit about that.

Torrey:

Yeah, it is a team sport because in general, even if it's the person doing the UX writing or that content design is also the product designer is also the interaction designer. What they're trying to do is take a wide variety of people who might be using this product that might be an incredibly diverse set of people, or it might be a very narrow set of people, let's say all IT pros. We want to sell this product to big corporations that have IT pros that want to manage their data centers. It's a pretty narrow slice of humans, but it's still hugely diverse in terms of from what language they're speaking and what kind of resources they have inside this company to the kind of background they have, to all of the different reasons they might need to manage their data centers right now.

Torrey:

From, hey, something new came online or there needs to be a new partition or new admin management of access to it or security patch updates to things like, oh, there was an earthquake at a data center and I need to and secure and audit any damage that might've happened. So there's a huge number of reasons. Let me back up of that deep analogy. There's a huge number of reasons even for a tiny population relative to the scope of humanity, a small population doing a relatively well-defined job still has a huge number of reasons they might need to be in an interface doing a thing. And what we have to do when we are designing the content for that and designing the experience itself is anticipate those and try and make sure that we've indicated that whatever reason they're coming there for, if it's a valid reason to use this piece of software, whatever reason they're coming there for, they see it reflected in the text and they understand what to do.

Torrey:

That is a team sport because I can't, and no individual person can anticipate all of those things simultaneously. We need to think them through sequentially. We need data to base it on. We need to understand, we need to hear from people who will use it or people who would use it to hear about how they think about it and specifically what language do they use, what's already in their head that we can use to reflect on that screen. So it's about understanding that space well enough, coming to understand that space well enough by communicating with other humans to know what are the right things to represent and in what hierarchy or embeddedness or relationalness, and then use some grammar and punctuation and other tricks up our language sleeves.

Larry:

Yeah, no....

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. My background is in philosophy and I was interested in, I got my PhD in philosophy, I was going to teach it. You write lots of papers, those sorts of things, and I miss that to some extent getting out into industry, and that's been my way back in to, all right, come up with an idea, try and distill it, think about objections, put it together, and so I'm really enjoying that lately.

Larry:

And I'm enjoying the video-

Casey:

Glad to be on the show.

Larry:

Yeah, no, I really appreciate what you're doing there. One thing I wanted to, and I love that that's how you're getting back to both your philosophical roots, but also part of it is to evangelize ontology practice, which is that's what this podcast is all about, democratizing and sharing practice. But I think, and I just love that you have this explicit and strong philosophical foundation and bent to how you talk about things. I think a lot of times that conversation is like, "Yeah, ontology comes out of philosophy," and that's the end of the conversation. But you've mentioned the role of metaphysics, epistemology, logic, all of which, can you talk a little bit about how those, beyond just I think a lot of people think about logic and OWL and all that stuff, but can you talk a little bit more about the role of metaphysics and epistemology and these other philosophical ideas?

Casey:

Yeah, definitely. You mentioned this in the pre-notes, "Here's a topic we'd like to get to," and I got into a lot of imposter syndrome on this, right? I'm trying to talk myself out of this, but I think most ontologists have this feeling there's no solid easy pipeline into becoming an ontologist, right? It's a very eclectic group of us. My background's in philosophy, you run into a bunch of librarians, you've got computer scientists who do DB administration, you've got jazz musicians I've run into, it's a weird group.

Casey:

I say that just to be, sometimes when I get asked about, "Okay, how does ontological practice work?" I think, well, I didn't actually train to be an ontologist. I fell into it, so I'm ill-equipped to say things about what role ontology or philosophy plays in ontology.

Casey:

I just know I learned philosophy, and then I'm using some of those tools here, so there's two different answers. One is historically, how does philosophy inform and shape the nature of ontology practice? And the other part is just, okay, if you've got a philosophical toolkit of metaphysics and epistemology and logic, how does that apply and make you a better, I mean, the obvious connection is that ontology is a philosophical term. It comes from metaphysics. We look back to Aristotle, and it's the study of that which exists, so do we want to say there's fundamentally fire, air, earth, water or something like that? Or fundamentally, there are these atoms and those are the sorts of things that are part of the inventory of reality. It's not physics, it's metaphysics. It's the thing that in I think for Aristotle is just, it's the book that sits next to his physics in all of his category, in his library of everything.

Casey:

But when we move that forward to computer science and data modeling, then we're thinking, okay, maybe not for all of reality, although maybe it depends on how big you want your data model to be. But if I'm a retailer, what are the terms and ontology, what are the terms that I care about, the things that I need to model the constituents of reality that matter to me? That might be types, if you're Amazon, it's okay, medium-sized dry goods versus sporting equipment versus something else. If I'm doing a medical ontology, it's patients and payers and providers, et cetera. In philosophy, in ontology, there's a bunch of different tools and examples, but we think about, okay, what are some fundamental distinctions that we want to make? How can we carve nature at its joints in really sensible ways? That's a phrase that you'll hear a lot. We could say more about it if you want.

Casey:

But what I found is being a philosopher goes into an ontology space is that I have this inventory of examples from all of my grad seminars and various things that I'm looking through and going through whether I want to talk about gavagai and undetached rabbit parts, if that makes sense to anybody, or whether I want to talk about grue as a color, here are some examples, ways that we can chop up the world in unnatural ways versus chopping it up in natural ways and how do we make those distinctions? That applies straightforwardly when you get into building an ontology model for an oil and gas industry or something like that. There's a bunch of ways that we can divvy up all the things you care about, what's the right and sensible way to do it?

Casey:

I guess that's the metaphysics, ontology way. Logic you mentioned, right? We need to think about reasoning. I don't just want to assert a bunch of things about my data. A fundamental premise of an ontology is that we want to understand our data, we want to confer meaning on it, and that means that we have to be able to leverage the structure of the ontology to infer things smartly. Simple things like set containment are fine if all persons are animals, and then we say something about animals, they're creatures. Then when I say that persons are a subclass of that, then I get for free that persons are spatio-temporal things as well. But we get a lot more complicated inferences as we go. We have to think about statistical reasoning. Just in general, if logic is the study of what makes for good arguments, what follows from what, that's obviously got a lot of applications in ontology, AI.

Casey:

And then the third piece that we talked about is epistemology. Epistemology is the study of knowledge and belief, roughly about what it means to be justified. The classic example there is, if I know something, what exactly does that amount to? And then Plato says it's justified true beliefs. And then the history of epistemology is littered with examples of trying to cash out exactly what does it mean to be justified. And if you get new information, how can that undercut your justifications? How do you update your beliefs?

Casey:

More recent stuff, and this is what I did in my dissertation,...

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 Berkeley National Laboratory. Many people just call it the Berkeley Lab. He's the principal investigator in a group there, has his own lab working on a bunch of interesting stuff, which we're going to talk about today. So welcome, Chris, tell the folks a little bit more about what you're up to these days.

Chris:

Hi, Larry. It's great to be here. Yeah, so as you said, I'm here at Berkeley Lab. We're located in the Bay Area. We're just above UC Berkeley campus. We have a nice view of the San Francisco Bay looking into San Francisco, and so we're a national lab, so we're part of the Department of Energy National Lab system, and we have multiple different areas here in the lab looking at different aspects of science from physics, energy technologies, material science. I'm in the biosciences area, so we are really interested in how we can advance biological science in areas relevant to national scale challenges really in different areas like energy, the environment, health and bio-manufacturing.

Chris:

My own particular research is really focused on the role of genes and in particular the role of genes in complex systems. So this could be the genes that we have in our own cells, the genes in human beings, how they all work together to hopefully create a healthy human being. One part of my research also looks at the role of genes in the environment, and in particular the role of genes inside tiny old microbes that you'll find in the ocean water and in the soil. And how these genes all work together, both to help drive these microbial systems, help them work together and how they all work together really to drive ecosystems and biogeochemical cycles.

Chris:

So I think the overall aim is really just to get a picture of these genes and how they interact in these kind of complex systems and build up models of complex systems from scales right the way from atoms through the way through to organisms and indeed all the way to earth-scale systems. So my work is all computational. I don't have a wet lab. So one thing that we realized early on is just when you are sequencing these genomes and trying to interpret the genes, you're generating a lot of information and you need to be able to organize that somehow. And so that's how we arrived at working on knowledge graphs, basically to assemble all of this information together and to be able to use it in algorithms to help us interpret biological data and help us figure out the role of genes in these organisms.

Larry:

Yeah, many of the people I've talked to on this podcast, they come out of the semantic technology world and apply it in some place or another. It sounds like you came to this world because of the need to work with all the data you've got. What was your learning curve? Was it just another thing in your computational toolkit?

Chris:

Yeah, in some ways. In fact, my background is, if you go back far enough, my original background is more on the computational side and my undergrad was in AI, but this is back when AI meant good old-fashioned AI and symbolic reasoning and developing Prolog rules to reason about the world and so on. And at that time, I wasn't so interested in that side of AI. I really wanted to push forward with some of the more nascent neural network type approaches. But in those days, we didn't really have the computational power and I thought, "Well, maybe I really need to, I actually learned something about biological systems before trying to simulate them." So that's how I got involved in genomics. This was around about the time of just before the sequencing of the human genome, and I just got really interested in this area, a position came up here at Lawrence Berkeley National Laboratory, and I just got really involved in analyzing some of these genomes.

Chris:

And in doing this, I came across this project called the Gene Ontology that was developed by some of my colleagues originally in Cambridge and at Lawrence Berkeley National Laboratory. And the goal here was really as we were sequencing these genomes and we were figuring out there's 20,000 genes in the human genome, we discovered we had no way to really categorize what the functions of these different genes were. And if you think about it, there's multiple different ways that you can describe the function of any kind of machine, whether it's a molecular machine inside one of your cells or your car or your iPhone or whatever. You can describe it in terms of what the intent of that machine is. You can describe it in terms of where that machine is localized and what it does, and how that machine works as part of a larger ensemble of machines to achieve some larger objective.

Chris:

So my colleagues came up with this thing called the gene ontology, and I looked at that and I said, "Hey, I've got this background in symbolic reasoning and good old-fashioned AI. Maybe I could play a role in helping organize all of this information and figuring out ways to connect it together as part of a larger graph." We didn't call them knowledge graphs at this time, but we're essentially building knowledge graphs at the time and make use of, in those days quite early semantic web technologies. This is even before the development of all the web ontology language, but there was still this notion that we could use, we could use rules in combination with graphs to make inferences about things. And I thought, "Well, this seems like an ideal opportunity to apply some of this technology."

Larry:

That's interesting. It's funny we didn't plan this, but the episode right before you in the queue was of my friend Emeka Okoye. He's a guy who was building knowledge graphs in the late '90s, early 2000s, mostly the early 2000s before the term had been coined, and I think maybe even before a lot of the RDF and OWL and all that stuff was there. So you mentioned Prolog earlier, and what was your toolkit then, and how has it evolved up to the present? That's a huge question. Yeah.

Chris:

I didn't mean to get into my whole early days with Prolog. Yeah, I've definitely had some interest in applying a lot of these logic programming technologies. As you're aware,...

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, combined with the new Model Context Protocol, has empowered semantic web experts like Emeka Okoye to build tools that let any developer surf the semantic web.

Interview transcript

Larry:

Hi, everyone. Welcome to episode number 36 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show my good friend, Emeka Okoye. Emeka is a really interesting ontology practitioner and knowledge engineer, and he's operating now at the intersection of knowledge engineering and generative AI, which I think is a really interesting intersection and that's what we're going to talk about today. So welcome, Emeka. Tell the folks a little bit more about what you're up to these days.

Emeka:

Oh, well, thank you bringing me to this awesome podcast. I'm proud to be here. I have been involved in knowledge engineering or more like AI. We need to understand that knowledge engineering is important for AI because it creates the knowledge layer. So that's where we have knowledge graphs. There's been a lot of tribalism in AI, the neural nets on one side and the symbolic AI on the other side. So I am in for the convergence. I've always believed in the convergence.

Emeka:

Funny enough, I've been teaching and mentoring young ones on both sides of the divide since 2016 in the Nigerian data science space. So no surprises that generative AI boomed, and I needed to find reasons to see how we can integrate both sides, because that's what AI is all about, the best of both worlds, best of neural nets, and then best of symbolic AI. That's the future. I mean, there's no doubt about it. So that foundation, I needed to be there and that's why I've been working on both sides. So from knowledge graphs to AI agents.

Larry:

That's so funny, we didn't talk about this before I hit record, but right before we started this interview, I posted a thing to LinkedIn about exactly that. It was specifically about the need for executive education around hybrid AI architectures 'cause all they have is Silicon Valley hype. That's all the information they have. But more to the point, you're a hybrid practice. Well, first of all, I've known you for years now, and it just occurred to me, I don't really know your academic background, but it sounds like you're equally grounded in machine learning and knowledge representation stuff. Have you always pursued both?

Emeka:

I'm a geologist. That's the only qualification I do have. Immediately I found love with personal computers. So once the PC era boomed, I just went in programming. Nigeria was once one of the biggest software countries in the world at a point in time. Our software houses were building financial and banking systems the whole of North America were using, and some part of Europe. So we are that big. So when the internet came, we embraced it that early. I was already building internet protocols using Visual Basic, and not long after I co-founded the first startup in Nigeria. And then after that I worked with probably one of the earliest Semantic Web brands in the world, which is OpenLink Software. I became the Chief Technical Officer in the whole of Africa.

Emeka:

So I was with OpenLink Software when Tim Berners-Lee came up with the Semantic Web thing and Ora and co coming up with agents. So I started early on, thanks to my mentor, my boss then, Kingsley Idehen, who mentored me throughout and made me understand that the future was Semantic Web. So I dove right into it. And can you believe this, we were already creating knowledge graph before Google called it knowledge graph. I had created one for a client, which is Music In Africa by 2011, 2012.

Larry:

That's right before they introduced the term knowledge graph with their... That's so interesting because... And the RDF and the OWL and all the Semantic Web tech goes back 10 years before that. So that gap between the dawn of the Semantic Web and the coining of the term knowledge graph, you were just in there doing it.

Emeka:

Yes. Yeah, we were already doing it. And remember I came from a company that is on top of this technology. You who Kingsley Idehen is. He's my former boss, and mentor today, even after. I left OpenLink Software, he was there to guide me in. So most of what I know in semantic technology comes from Kingsley. So we were already doing this. So my understanding of the technology is very sound. Academia-wise, I didn't do anything much in that regards on the technology, but I'm hoping I'll do research in the future, because as I'm trying to come into Europe, I noticed that there are a lot of research-based jobs and AI is something I would love to devote research time.

Larry:

Yeah, and I know a lot of those people, and there's not a specific track yet around the hybrid AI stuff. I hope you get a chance to do that. But hey, that's what I want to really focus on today. So your background, your RDF Explorer project makes even more sense to me now. I just want to say real quickly about that. Emeka and I meet once or twice a week, and our Dataworthy Collective, which we co-organize with some other folks, and I was just embarrassed that I had totally missed this awesome piece you wrote for LinkedIn about RDF Explorer, and then you just happened to mention it in one of our meetings, and I went and read it and I was like, "Whoa, that's amazing. We got to talk about this."

Larry:

So here we are. Finally, I get to share the RDF Explorer with folks. So tell me, I think one thing I've been a little bit surprised by is that not everybody in the knowledge graph and semantic tech space is familiar with MCP. They maybe know the acronym and what it stands for, but can you talk just a little bit about the Model Context Protocol?

Emeka:

All right, so the Model Context Protocol, which was created by Anthropic sometimes in November 2024, is a standardized protocol which allows AI agents to connect and interact with external tools and different data sources in a simplified manner. It's that simplicity that is the attraction. So it removes a lot of stress that comes to connecting different data source to it. Now, just to give you an idea what we are talking about. Before MCP, we had all these agentic RAG solutions. It means that you hand-code every individual sources to pull that

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

The slide mentioned in the interview

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. In response to the needs of data stewards and consumers across science, industry, and technology, the FAIR data principle - F A I R - was introduced. Tom Plasterer was instrumental in the early efforts to make web data findable, accessible, interoperable, and reusable.

Interview transcript

Larry:

Hi everyone. Welcome to episode number 35 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show, Tom Plaster. Tom is the managing director who leads the knowledge graph and FAIR practices at Exponential Data, which is a company in the Boston area, or he's in the Boston area. So welcome Tom, tell the folks a little bit more about what you're up to these days.

Tom:

Thanks, Larry. And great pleasure to be with you and the audience. So I'm now, just last week I hit a year at Exponential Data, after 12 and a half years at big pharma. And so, I came over to Exponential Data to lead the knowledge graph and FAIR data practices, and also to unite with our expertise around artificial intelligence. One of the things that I started to get really excited about with the knowledge graph conference over the last few years was the convergence of these two communities, and really how AI knowledge graphs and especially FAIR data, as a way of having curated trusted data for these applications, could be completely synergistic. And so that was really what brought me there. And when I joined, we were around 40 people. As I was leading this practice, we grew to about 240. And were recently acquired by Genpact.

Tom:

And so, now we're now part of a much bigger organization bringing our strength of artificial intelligence, generative AI, knowledge graphs and FAIR data to this larger organization. So that's been really my journey over the last year. And really wanted to bring these two technologies together. And one of the things that we've really found is how important FAIR data is to both sides of the equation. And so, this is really where trusted data, clean data, data that follows standards, data that's self-describing, all of the things that you want to do for FAIR data, are really important foundationally for what you want to do with knowledge graphs and for how you want to give this trusted data to large language models, generative AI, to get the most out of those technologies. So in a nutshell, that's been my journey over the last year.

Larry:

Yeah. And we didn't talk explicitly about it as we were preparing for this, but AI is the logical and obvious place where all this is going now. And I think everybody's concerned about delivering trustworthy, clean, FAIR data wherever you are. But do you feel like have you been uniquely well-prepared for that with both your company but... And I know your background, that's what we want to talk about today, is the origins of the FAIR data standard and you've been around it right from the get-go right?

Tom:

Right from the beginning. And the community leans a lot on earlier trends around the Semantic Web, Semantic Web technology. I think a lot of the founders are very web centric in their thinking. And there's a direct tie between with Tim Berners-Lee, Ora Lassila, Jim Hendler wanted to accomplish with the Semantic Web, how the standards evolved there and then grew up and became available within graph databases, eventually knowledge graphs, as a vehicle to prove that FAIR data worked. And so, that's a direct thread between that and wanting to have knowledge injection for generative AI and the value there. The whole thing flows really, really well.

Larry:

Yeah, interesting. And one thing as you said that the direct descendants from Tim Berners-Lee's and Ora and Jim's, I guess the paper in Scientific American, one of the things that arose like, I don't know what, five or 10 years after that was Tim Berners-Lee's notion of five star data, like the kind of 1, 2, 3, 4, 5 star rating. And then only, what, five, not five, seven years later, FAIR came along. Can you talk a little bit about how these perceptions of and the way good data and their practices are codified?

Tom:

Sure. So if we think about five star linked data and kind of what Tim was trying to accomplish there, get your data on the web, having an accessible format, follow standards, have it linked together, that's really, really close to the FAIR data principles itself. And I think a lot of the things within the FAIR data principles were learned directly from that. And I guess first I should take a step back and explain. People have probably come across the FAIR data principles, and they've heard Findable, Accessible, Interoperable, Reusable, and they think there's four of them. There's 15 of them. So this is where it gets to be a little bit more complicated. So FAIR as an acronym was just a very nice way of marketing and putting these things together, but a lot of the ways that they can become really useful is the cell principle. So I'm just going to talk about them and describe them real briefly without being too technical. People can learn more about it in the 2016 Nature Medicine paper.

Tom:

So the findable is really about URIs. And so it's really about can I identify both an instance of data or a concept, a class that follows a URI, later an IRI, and sometimes we're calling them persistent identifiers or GUPRIs, so Global, Unique, Persistent Resource Identifiers, all the same thing. So can you use that to identify a piece of data, and if so, when you resolve it, will it provide useful metadata for both humans and machines? That's really the most important piece that you need to do to get started. Let's put an identifier on our data, on our metadata, so that we can resolve it, find it, put it in an index, so that we can get something useful out of it. So that's about four of the F principles there.

Tom:

Accessible is really about interoperability and it's following common protocols. So HTTP, HTTPS, we're not reinventing protocols, we're following standards. And then authentication on top of that in some sort of a certified manner. Usually it ends up being LDAP with single sign-on or something like that. Some way of authenticating your data....

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 profile. You're like, "I'm a people-loving architect, and data leader." And one of the things I love about that, we talked a fair amount at the knowledge graph conference about your background in the humanities-

Mara:

We did.

Larry:

... and your transition into your current role. I would love to hear ... And what you just said, like, the end of what you were just saying about so much of your job is about interacting with people, and convincing business leaders to fund you, and stuff.

Can you talk a little bit about that? Like, what drew you into the humanities in the first place, your transition out of it, and here we are today.

Mara:

100%. Before I talk about being in the humanities, I love to read, I was an epistemologist, and a 19th Century scholar. But before that, when I was a little girl, I was writing my own websites in HTML, XML, and some of the technologies that eventually got to be used in the semantic web, which is how I entered the knowledge graph space way later as an adult. So, that got put on hold.

Mara:

I love to read. So, I became a humanities scholar, and I was for about five years, the lowest of the low adjuncts at an R1. My teaching experience, not my scholarship, although, I did a lot of thinking about how people interact with written media, and how they enter internal argumentation with those media, and come to know the world differently. That's what most of my work was about. It was interdisciplinary with literature, history, philosophy of science, which is why I say epistemology.

Mara:

But my best teacher for coming to where I am today was being a teacher. So, lowest of the low, first year, although, I spent most of the time teaching applied rhetoric, I was teaching freshman comp. So, this is a super diverse group of students who are showing up for a required class. To be successful, I needed to do two things. One, I had to listen carefully to what these students cared about to actually get them to get something out of the between $5000 and $8000 they were paying for this course. And then I had to generalize what I wanted them to learn about enough to make it accessible to them. Okay? So, my goal throughout my teaching career, similar to my goal now, is to inculcate effective communication through fit for purpose argumentation.

Mara:

So, while a lot of my colleagues were being like, "Here's an essay. Write something about it. Make it sound smart," what I did, because I needed my students to hook in, to be engaged, because the vast ... I maybe, I don't know, taught, like, four English majors over my career ... No HSPS [humanities, social, and political science] people. I told my students, "Okay, guys. Get into groups. So, you're set up to do some argumentation amongst yourselves, pick a little part of this essay," this was the first year, "And something you react strongly to. What about the sentences are doing this? Grammar, syntax, semantics. What's the whole universe of your group reactions? How are they related, or not related?"

Mara:

This evolved into a directed research curriculum in my applied rhetoric courses. So, I said, "Okay. Okay, guys. Go find something out in the world that needs to change. We need a pedestrian bridge over the street. We need better accessibility for people with disabilities in our gym. We need better gym hours. Figure out how it's working, frame up a case for someone who can make a change, do your argumentation, go present it." Some of my students actually argued well enough that they got a stoplight installed on a really busy street corner. So, it worked.

Mara:

So, fast-forward, lots of life drama that brought me out of the humanities into what is a much better place for me, in corporate. I'm in a trusted advisor role, not so dissimilar to being a teacher. As a trusted advisor, I have to be attuned to what the business says that they want. So, if they're saying ... And then what they demonstrate that they want through their behaviors, and through their artifacts, often times, their processes and information system. And then I have to think about why and how those things align, or don't align.

Mara:

Because I'm full-time employed, and this is in this role, and all of my corporate roles, but I'm, effectively, providing a boutique service. It's not enough for me to come up with something that sounds smart, or cool. I have to come up with a solution that accommodates process data, technology, and, most importantly, people, and that actually fulfills a business need. And I used to think about the connection from my academic career to my corporate career as like, "Oh, I became a good EA, because I taught my students to do this," but with about a decade of reflection, I'm realizing that teaching was really mutual.

Mara:

Like, I asked my students to show me what they were thinking. I evaluated what they were doing. I was very critical, but I was generous. And I was with them as their efforts bore fruit, or didn't. But how I demonstrated, elicited, and critiqued them evolved with constant, and often very, very vulnerable feedback. Like, I do with my clients now, I constantly asked, "How am I doing? Am I giving you what you need? Do you need something else?'

Mara:

For a student, it's really hard to say that to someone who's got the power of a grade over you. It's not as perhaps scary when we're all adults in corporate, but I still think many adults ... I was always the stupidest person in the room as a scholar. So, I don't have this problem, but a lot of us are worried about appearing, "I'm not smart enough. I am not creative enough." So, I still have to flex that good, compassionate, people-loving, listening muscle all the time in corporate just like my wonderful undergrads at Indiana University taught me how to do.

Larry:

That's so awesome. I've thought a lot about rhetoric. In fact, I don't know if we talked about this in New York, but my first career was in college textbook publishing,...

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, everyone. Welcome to episode number 33 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Frank van Harmelen. Frank is a professor of AI at the Vrije Universiteit in Amsterdam, that's the Free University in Amsterdam. He's also the PI of this big program called the Hybrid Intelligence Center, which spans seven Dutch universities, multimillion euro grant over 10 years. Welcome, Frank. Tell the folks a little bit more about what you're up to these days?

Frank:

All right. This Hybrid Intelligence Center occupies me most of the time, and that's been a very exciting ride over the past five years. We're just at the midpoint and we have five more years to go.

Larry:

Nice. How is it going? Are you satisfied? Are the expectations of the grantors being met and are you happy with the progress you're making?

Frank:

Yes. It's obvious to say that the world of AI is super dynamic now. All kinds of things have happened in the past few years in AI that nobody had predicted when we started, the rise of large language models of conversational AI. That has also really affected the notion of hybrid intelligence. It's been an even more exciting ride than we had expected.

Larry:

Yeah. That's right. Yeah. I think excitement is the word of the day. Hey, one thing I have to observe, earlier today before we recorded this, I was doing a presentation with some information architects, and the subject I was talking about hybrid AI architectures and neuro-symbolic loops and all this stuff. One of the people in the presentation asked, "Hey, what about human AI? Shouldn't that be the architecture?" Then I said, "You're going to love my next podcast guest," because that's the whole point of this hybrid intelligence idea, right?

Frank:

Yeah. The core idea of hybrid intelligence, hybrid standing for hybrid combination of human and machine intelligence. Think of hybrid teams, where a hybrid team is made up a bunch of people and a bunch of AIs who collaborate to get a task done. That, if you want, the tagline of the Hybrid Intelligence Center is that we're working on AI that collaborates with people instead of replacing them. If you work on AI systems that collaborate with people, then you certainly need to solve all kinds of different problems and answer all kinds of different questions than where you are thinking about AI in the replacement mode.

Larry:

Yeah. That seems to be, like in a lot of circles, there's this assumption that AI is just here to replace people, but you've been... Long before that was a meme and people talking about it, you were working on this hybrid concept. Has that heightened the urgency around your work, the current state of AI expectations?

Frank:

It has heightened the urgency, and it has also opened all kinds of doors. One of the big hurdles in AI-human collaboration, say five years ago, was really the conversational interface. It was hard to talk to AI systems, and they certainly wouldn't talk back to you in a coherent way. Well, we all know that's now a solved problem. But what happens in the middle is the real challenge. We don't think that the large language models are going to solve all of the collaboration between humans and AI systems. We want our AI systems to do things that the language models are not very good at, but we're using that technique in a kind of sandwich model. Now, the language model does the conversation on the front end, it does the conversation on the back end, and we're working on the AI agents, the smart that's in the middle, to create these hybrid teams.

Larry:

As you say that I'm thinking about that's just one aspect of the hybridization of this. That that's one way that humans... When you think about hybrid architectures, where LLMs can help build knowledge graphs and they can also fill in knowledge gaps in LLM architectures. What other obvious complimentary things are there between... What do humans need help with and what do machines need help with?

Frank:

Right. There are some obvious things like the perfect memory that machines have and the imperfect memory that we have. Okay? That's a nice example of where members in the team can really compensate for each other's strengths and weaknesses. Humans suffer from a whole host of these cognitive biases. For example, we suffer from the recency effect. We believe information more if we've heard it recently rather than when we've heard it in the past. We believe information more when we've heard it more frequently rather than... There's no reason to believe something more if you hear it more often.

Frank:

That doesn't make it more true, but it's how our brain works. Not always such a good idea. Computers can help us to compensate for all of these cognitive limitations. Conversely, we are very much aware of the context in which we operate. We are aware why we are doing something. We are aware of the implicit norms and values that govern the task that we're doing, that we're expected to obey in a particular group to perform a particular task. Computers don't have any sense of why they are doing something, the context in which they're doing it, the social and ethical norms under which they should operate. That's something where the human component can compensate for the machine limitations. These are just a few examples of that complementarity.

Larry:

Yeah. That's one of the things I think about a lot is that what we call in my world stakeholder alignment or stakeholder discovery or working with subject matter experts to make explicit their tacit knowledge in their head and things like that. It seems like that's probably always or mostly going to be a human capability. Is that... You probably have research that backs this up, right?

Frank:

Well, and if you want to collaborate with a computer, then you better make sure that there is some alignment between you and the computer. We can only collaborate because we share some of the way we look

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 London. So welcome, Denny. Tell the folks a little bit more about what you're up to these days.

Denny:

Thank you so much for having me, Larry. It's really a pleasure and honor. I enjoy listening to your podcast a lot, and I'm very happy to be here too. So these days I'm with the Wikimedia Foundation and, as I said, called Head of Special Projects. There are working on two new projects, one called Wikifunctions and Abstract Wikipedia, which are really very much tied together, and we'll get to those both in a moment, I think.

Larry:

Yeah, I'm really excited about both projects. I can't wait to get to them, but let's talk a little bit about Wikidata first because you started that 2012, is that correct?

Denny:

That's right, yes.

Larry:

What was the impetus for that? What motivated you to start that project?

Denny:

Well, this goes actually back to 2005. Markus Krötzsch and I were PhD students in Karlsruhe and Wikimania was coming to Frankfurt, which is really close to Karlsruhe. It was the very first Wikimania at all. We were both Wikipedians and we wanted to go there and we thought, "What could we do?" And so we connected our research topic, which was the Semantic Web, with Wikipedia and made a proposal there. We didn't really think it would go anywhere. We were just like, "This would be really cool if this happened."

Denny:

But over the next few years, there was so much interest in that people actually started implementing our ideas. We picked up on that. Semantic MediaWiki came out of it. And eventually when I was finishing my PhD, I was asked by Mark Reeves, who was working for Paul Allen's Vulcan back then, he was asking if I would like to make this happen for real. And so we approached the Wikimedia Foundation, we approached Wikimedia movement, and there was great excitement about it. We got the funding aligned and then we started working on Wikidata. This was really a dream come true basically for us who've been working on this idea of bringing structured data and Wikipedia together for more than seven years at that point.

Larry:

That's so interesting because my interest, I mean obviously it goes back aways, but my history of this kind of picks up with Wikidata, so that prehistory of it, connecting Wikipedia to the Semantic Web, which is obviously you're going to end up with something like Wikidata. And you were backed by the Vulcan Foundation or by Paul Allen's foundation?

Denny:

Yes.

Larry:

I did not know that. I lived in Seattle for a long time, so I walked by their building a lot. Well, that's really fascinating. So from 2005 to 2012, was it like simmering or were you doing things like precursors to the launch of Wikidata?

Denny:

We were doing precursive work. We were developing an extension called Semantic MediaWiki, which it is still quite widely used. There are two conferences per year about Semantic MediaWiki users. NASA is using it, for example, on the ISS. Microsoft and many others were also using or are still using it, which is actually integrating structured data into a MediaWiki installation and allowing everyone to build small knowledge graphs to query it and so on.

Denny:

For Wikidata, we took a lot of those lessons. We knew that we needed a little bit different data models. We started actually a different software project where we didn't build it on Semantic MediaWiki but rather something even more structured. Semantic MediaWiki is really good if you want to interleave the text together with the structured data, with the annotations. Whereas, Wikidata really builds a pure knowledge graph, items connecting it and giving it values and so on. But originally we were thinking, "Oh, we'll just switch on Semantic MediaWiki on the Wikipedias. I'm very glad we didn't do that.

Denny:

Actually, just recently, the 10-year anniversary of Wikidata was coming up recently. It's also already three years ago. Markus and Lydia Pintscher, who's the product lead for Wikidata at Wikimedia Deutschland, and I wrote a paper about the history of Wikidata. We were actually going into detail about these topics and how Wikidata came around.

Larry:

Oh, I'll have to link to that paper in the show notes. I'd love to read it. Well, then that's interesting. And then so Wikidata, that was sort of the original... not original, but it was one of the first realizations of the promise of the Semantic Web, and it continues to be in the sense of the unique identifiers and entity resolution and things like that. I assume you consider it a success. It seems like it's such an important part of the knowledge part of the internet.

Denny:

If you ask me, yes, definitely, Wikidata is a resounding success, obviously. It's certainly a much bigger success than we expected. More than half a million people have contributed to Wikidata. If you had asked me in 2010/2011 what the number of people will be who will contribute to such a project, I would be off by more than 10X. I would never have assumed that half a million people would actually contribute to such a project. So I'm really happy. Wikidata is now the most edited wiki in the world by far, even beating English Wikipedia. It is also just very large, very comprehensive. I'm more than excited about how it has developed, and I'm very happy to see how really I was continuing to work after I left Wikidata.

Larry:

Nice. For all of its success though, you see more that could be done in this area, right? Is that where your current projects come from?

Denny:

Yes, absolutely. So Wikidata is a classical knowledge graph. Actually, we went beyond the classical data model already in Wikidata, right? So we are not just like triples, it's not just subject predicate object, we also introduced the ability to have qualifiers on each of those statements. We introduced the ability to have references for every statement and so on. So there was a number of things that we added.

Denny:

...

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. But the reason I wanted to have you on, I caught a video, we met somewhere recently, but anyhow, I was watching a video with the OriginTrail folks, and you made this great quote in there. Somebody asked about the Semantic Web and just kind of offhandedly dismissed it like people always do when they talk about it. And you said, "Yeah, but that's the most catastrophically successful thing which people have called a failure." Can you elaborate on that a little bit?

Charles:

Yes. I think what it really boils down to with that is what ... Well, how do you classify success or failure? These are actually very incredibly abstract terms. And what I was referring to originally was a bit of data, a statistic that over 50% of web pages that exist contain RDF. And what does that mean? That means that there are statements that are written in RDF syntax in over half web pages. And that didn't sound particularly unsuccessful to me, and I'd struggled with these kind of statements in my mind for a while, such as why is RDF a failure? You hear this kind of thing go around. And I thought it didn't really feel like it to me, it never really felt like it was particularly a failure. I mean, I built two businesses from it and they weren't failures and we represented an awful lot of knowledge in that time and it felt like that knowledge was represented. So yeah, how do you quantify successful failure? Who's taking the measurements and who's making that argument has a lot to do with that.

Larry:

And the other thing that I shared that ... I was doing a presentation shortly after I saw that talk, and I grabbed that stat because I think a year ago or something, Tony Seale pointed out to something like 40 some percent of websites had RDF annotations at that point. And I was like, wow. So it's still growing too. So there's that sort of growth of it. But immediately I followed that with a slide, which I noticed when I went back and watched another presentation of yours that you had done a similar slide where you just kind of list the enterprises that have done stuff with RDF, like RDF graphs, like your slide listed Facebook and Amazon and Uber and Siemens and Google. And I had included in mine, like LinkedIn's economic graph and Netflix's graph and JPMorganChase and Credit Suisse and Bloomberg and all these others. So it feels like a pretty successful effort in that regard too.

Charles:

Yeah, exactly. In many ways it's those with the deepest pockets that have benefited the most from the technologies, which is a statement which you hear from time to time in the modern world, of course. That's not that surprising. It's those who can dedicate their time on maybe understanding a larger data landscape, seeing the value of it and joining it all together and representing it properly. Is it really any surprise that they're the ones that have got the most value out of it? No, it's not, basically. Yeah, I mean-

Larry:

Well, they have both the problem and the means to implement a solution. That's kind of the idea.

Charles:

Yeah. Right. Exactly. And if they find that they don't like whatever tooling they find that can support what they're doing, they have the capability to build things themselves or join things together and buy things that maybe others couldn't afford and this sort of thing. So especially in the early days when things are very much cutting edge and new technology sets, you don't necessarily have big swaths of open source technology stacks available to you and stuff like that. These things are growing all the time, of course. But if we are speaking about it from a success is count metric of implementation nodes sense, then those things will have a big bearing on that. But I'm not even necessarily sure that that's the right metric to quantify success for.

Larry:

Because we were talking before we went on the air too, about what is the right metric because you said that, okay, which portion of the data in the world is represented as RDF in a graph someplace. That's pretty low. It's still mostly in SQL databases, but if the criterion was like, how do we understand all that data, you can make a case for RDF. Is that right?

Charles:

Yeah, I think that's a very good point. I don't even think we necessarily talked about it in exactly the way that we're about to talk about it, I think. But you are right. I mean, okay, so if what we are going to say is that the success of this is measured on how much data do we understand, that could be understood with no previous understanding apart from how to follow ontological models, then RDF is a massive success because this is the only one that really works. So on that very metric, all the others have a kind of success rate of zero to a certain extent because there was never really any overriding standardized ontological modeling concept which filtered down into it.

Charles:

Now, you could argue that maybe for example, entity relationship diagrams tried to replicate that sort of thing. So creating conceptual things which are related to other conceptual things, but they were always really created in a form that was supposed to be implemented into maybe a relational database or something like that. They tended to be bound to technology to a certain extent. So if that was an interesting metric, then maybe RDF is vastly successful because it could represent stuff in a way that's really well understood immediately. Yeah.

Larry:

The way you just said that too reminds me that the notion of a technology being bound to the success. You could even argue that RDF just happens to be a thing that works now, but that's not necessarily the only way you could represent knowledge. Have you given much thought to other ways or why RDF is successful in that regard?

Charles:

Yeah, sure. We've been recording knowledge for a very, very long time. And I mean that as in we as in people. First of all, people were recording knowledge by basically telling stories to one another and saying things,...

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. So welcome, Andrea. Tell the folks a little bit more about what you're up to these days.

Andrea:

Thank you. Thank you very much, Larry, for having me. It's a pleasure. Yes, as a CTO in Quantyca, I'm in charge of all our advisory services. So I'm helping a customer in figure out how to manage their data properly, especially to leverage the potential of artificial intelligence. So basically I see all sort of problem in data management. Each client, it's different, but each client have a lot of problem of data that is very fragmented or too complex to manage. And so it's a very complex problem to feed this data to the AI model and extract the potential that the modern AI and all the breakthroughs that we are seeing in this day made available. So I'm really focused at this moment to help customers, especially in find a way to manage their knowledge, the knowledge that is characteristic of the company, that is a differentiator of the companies, the knowledge that is not known at the large language mode, what make the company different and can be leveraged to implement domain-specific, company-specific use case based on AI and leveraging the data collected.

Larry:

Yeah. As you mentioned that, we were just chatting a bit before we went on about the scope of the conversation. And I totally forgot to mention AI, which is of course is like the main driver for half of this stuff we're doing nowadays. But a couple of things you mentioned there. I want to go back to, one, you mentioned what a complex problem space this is and the challenges of data management and every organization has its own issues there. One of the ways that folks like you have helped people cope with this is the notion of a data product. And I know that's a newish concept and maybe new to some of the listeners to this. Can you talk a little bit about your conception of what a data product is and how you put one together?

Andrea:

Yeah, absolutely. The concept is new but the rationale behind it, it's not new. Humans, when a problem is too much complex, the only way that humans have found to solve a very complex problem is to split in a different part, in smaller part, and try to take all the complexity within each single part. And the idea of that a product come from this strategy, done the lead, the team part. So the idea is not managing the data in a unique central platform in which all the data of the company is collected but split in a modular architecture. So the platform is still there. You have the data layer, the data warehouse, whatever. It's the architecture that you prefer, but it's not anymore a monolithic solution in which you store all the data that you have in your company, but it's built as a composition of independent modules. Each module focuses on one or more, but usually one specific data asset, and there is a team that is in charge of manage the life cycle of that data product that manages specific data asset.

Andrea:

Of course the composition of all the data asset create the platform and the platform can be used to support the different use cases, but basically you can work on each single module without caring too much about the other module because each module is isolated with a specific interface. So if you do not modify the interface, you can modify the technology and implementation inside. And if you want to understand how the different modules connect, you can ignore the implementation and just concentrate on the relation between the different interfaces.

Andrea:

So to make it very, very simple, we can think at the data product like a sort of microservice that is a software application, is actually a software application, that does not expose functionality, transactional functionality, to acquire data and drive the transaction but expose the data. It's a software application that expose the data in order to make the data it manages as much usable as possible for its customer base, for its users. So this is a data product. And of course because it is a product, it is managed with a product mindset. So it's not a project. It's not something that the team develop and then forget about it. But there is a dedicated team that implement the first version and then evolve the software application that support that specific data asset through all its lifecycle till the retirement when the data asset is not anymore relevant for the company. That's pretty much what is a data product for me.

Andrea:

So basically I call this kind of data product a pure data product to even more underline the fact that it's a software application that expose data because I also have a lot of time the question, a report, a dashboard is a data product and they say, yes, it's a data product if it is managed as a product with a product mindset. But my book, my research is more focused on the pure data product, so that specific kind of data products that do not expose visualization or insight or action but expose just pure data to make it reusable and composable over time to support multiple use cases.

Larry:

That's right. We didn't talk about this before we went on the air, but the episode right before this one is with Dave McComb, and I know I've heard you talk before about you appreciate his approach and his data-centricity. And everything you just said, I'm like, "Oh yeah, he's read Dave's books." Was that the major influence, or what are the influences?

Andrea:

Absolutely. It was for me an epiphany because at that time when I read McComb's books, I was looking for... I had a problem because we had started since couple of years to help our customer and created this kind of modular architecture. So that architecture that is built as a composition of different data product, even managed with a distributed operating model. So all the data product are managed by different business domain in an autonomous way. And we have seen that this improve a lot the quality of the data and reduce the maintenance cost because of cour