Denny Vrandečić

As the founder of Wikidata, Denny Vrandečić has thought a lot about how to better connect the world's knowledge.

His current project is Abstract Wikipedia, an initiative that aims to let anyone anywhere on the planet contribute to, and benefit from, the world's collective knowledge, in their native language.

It's an ambitious goal, but - inspired by the success of other contributor-driven Wikimedia Foundation projects - Denny is confident that community can make it happen

We talked about:

his work as Head of Special Projects at the Wikimedia Foundation and his current projects: Wikifunctions and Abstract Wikipedia

the origin story of his first project at Wikimedia - Wikidata

a precursor project that informed Wikidata - Semantic MediaWiki

the resounding success of the Wikidata project, the most edited wiki in the world, with half a million contributors

how the need for more expressivity than Wikidata offers led to the idea for Abstract Wikipedia

an overview of the Abstract Wikipedia project

the abstract language-independent notation that underlies Abstract Wikipedia

how Abstract Wikipedia will permit almost instant updating of Wikipedia pages with the facts it provides

the capability of Abstract Wikipedia to permit both editing and use of knowledge in an author's native language

their exploration of using LLMs to use natural language to create structured representations of knowledge

how the design of Abstract Wikipedia encourages and facilitates contributions to the project

the Wikifunctions project, a necessary precondition to Abstract Wikipedia

the role of Wikidata as the Rosetta Stone of the web

some background on the Wikifunctions project

the community outreach work that Wikimedia Foundation does and the role of the community in the development of Abstract Wikipedia and Wikifunctions

the technical foundations for his

how to contribute to Wikimedia Foundation projects

his goal to remove language barriers to allow all people to work together in a shared knowledge space

a reminder that Tim Berners-Lee's original web browser included an editing function

Denny's bio

Denny Vrandečić is Head of Special Projects at the Wikimedia Foundation, leading the development of Wikifunctions and Abstract Wikipedia. He is the founder of Wikidata, co-creator of Semantic MediaWiki, and former elected member of the Wikimedia Foundation Board of Trustees. He worked for Google on the Google Knowledge Graph. He has a PhD in Semantic Web and Knowledge Representation from the Karlsruhe Institute of Technology.

Connect with Denny online

user Denny at Wikimedia

Wikidata profile

Mastodon

LinkedIn

email: denny at wikimedia dot org

Resources mentioned in this interview

Wikimedia Foundation

Wikidata

Semantic MediaWiki

Wikidata: The Making Of

Wikifunctions

Abstract Wikipedia

Meta-Wiki

Video

Here’s the video version of our conversation:

https://youtu.be/iB6luu0w_Jk

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 32. The original plan for the World Wide Web was that it would be a two-way street, with opportunities to both discover and share knowledge. That promise was lost early on - and then restored a few years later when Wikipedia added an "edit" button to the internet. Denny Vrandečić is working to make that edit function even more powerful with Abstract Wikipedia, an innovative platform that lets web citizens both create and consume the world's knowledge, in their own language.

Interview transcript

Larry:

Hi, everyone. Welcome to episode number 32 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Denny Vrandecic. Denny is best known as the founder of Wikidata, which we'll talk about more in just a minute. He's currently the Head of Special Projects at the Wikimedia Foundation. He's also a visiting professor at King's College Lo...

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.

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.

Dave McComb

During the course of his 25-year consulting career, Dave McComb has discovered both a foundational problem in enterprise architectures and the solution to it.

The problem lies in application-focused software engineering that results in an inefficient explosion of redundant solutions that draw on overlapping data sources.

The solution that Dave has introduced is a data-centric architecture approach that treats data like the precious business asset that it is.

We talked about:

his work as the CEO of Semantic Arts, a prominent semantic technology and knowledge graph consultancy based in the US

the application-centric quagmire that most modern enterprises find themselves trapped in

data centricity, the antidote to application centricity

his early work in semantic modeling

how the discovery of the "core model" in an enterprise facilitates modeling and building data-centric enterprise systems

the importance of "baby step" approaches and working with actual customer data in enterprise data projects

how building to "enduring business themes" rather than to the needs of individual applications creates a more solid foundation for enterprise architectures

his current interest in developing a semantic model for the accounting field, drawing on his history in the field and on Semantic Arts' gist upper ontology

the importance of the concept of a "commitment" in an accounting model

how his approach to financial modeling permits near-real-time reporting

his Data-Centric Architecture Forum, a practitioner-focused event held each June in Ft. Collins, Colorado

Dave's bio

Dave McComb is the CEO of Semantic Arts. In 2000 he co-founded Semantic Arts with the aim of bringing semantic technology to Enterprises. From 2000- 2010 Semantic Arts focused on ways to improve enterprise architecture through ontology modeling and design. Around 2010 Semantic Arts began helping clients more directly with implementation, which led to the use of Knowledge Graphs in Enterprises. Semantic Arts has conducted over 100 successful projects with a number of well know firms including Morgan Stanley, Electronic Arts, Amgen, Standard & Poors, Schneider-Electric, MD Anderson, the International Monetary Fund, Procter & Gamble, Goldman Sachs as well as a number of government agencies. Dave is the author of Semantics in Business Systems (2003), which made the case for using Semantics to improve the design of information systems, Software Wasteland (2018) which points out how application-centric thinking has led to the deplorable state of enterprise systems and The Data-Centric Revolution (2019) which outlines a alternative to the application-centric quagmire.

Prior to founding Semantic Arts he was VP of Engineering for Velocity Healthcare, a dot com startup that pioneered the model driven approach to software development. He was granted three patents on the architecture developed at Velocity. Prior to that he was with a small consulting firm: First Principles Consulting. Prior to that he was part of the problem.

Connect with Dave online

LinkedIn

email: mccomb at semanticarts dot com

Semantic Arts

Resources mentioned in this interview

Dave's books:

The Data-Centric Revolution: Restoring Sanity to Enterprise Information Systems

Software Wasteland: How the Application-Centric Quagmire is Hobbling Our Enterprises

Semantics in Business Systems: The Savvy Manager's Guide

gist ontology

Data-Centric Architecture Forum

Video

Here’s the video version of our conversation:

https://youtu.be/X_hZG7cFOCE

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 29. Every modern enterprise wrestles with its data, trying to get the most out of it. The smartest businesses have figured out that it isn't just "the new oil" - data is the very bedrock of their enterprise architecture. For the past 25 years, Dave McComb has helped companies understand the...

Ole Olesen-Bagneux

In every enterprise, says Ole Olesen-Bagneux, the information you need to understand your organization's metadata is already there. It just needs to be discovered and documented.

Ole's Meta Grid can be as simple as a shared, curated collection of documents, diagrams, and data but might also be expressed as a knowledge graph.

Ole appreciates "North Star" architectures like microservices and the Data Mesh but presents the Meta Grid as a simpler way to manage enterprise metadata.

We talked about:

his work as Chief Evangelist at Actian

his forthcoming book, "Fundamentals of Metadata Management"

how he defines his Meta Grid: an integration architecture that connects metadata across metadata repositories

his definition of metadata and its key characteristic, that it's always in two places at once

how the Meta Grid compares with microservices architectures and organizing concepts like Data Mesh

the nature of the Meta Grid as a small, simple, and slow architecture which is not technically difficult to achieve

his assertion that you can't build a Meta Grid because it already exists in every organization

the elements of the Meta Grid: documents, diagrams or pictures, and examples of data

how knowledge graphs fit into the Meta Grid

his appreciation for "North Star" architectures like Data Mesh but also how he sees the Meta Grid as a more pragmatic approach to enterprise metadata management

the evolution of his new book from a knowledge graph book to

his elaboration on the "slow" nature of the Meta Grid, in particular how its metadata focus contrasts with faster real-time systems like ERPs

the shape of the team topology that makes Meta Grid work

Ole's bio

Ole Olesen-Bagneux is a globally recognized thought leader in metadata management and enterprise data architecture. As VP, Chief Evangelist at Actian, he drives industry awareness and adoption of modern approaches to data intelligence, drawing on his extensive expertise in data management, metadata, data catalogs, and decentralized architectures. An accomplished author, Ole has written The Enterprise Data Catalog (O’Reilly, 2023). He is currently working on Fundamentals of Metadata Management (O’Reilly, 2025), introducing a novel metadata architecture known as the Meta Grid. With a PhD in Library and Information Science from the University of Copenhagen, his unique perspective bridges traditional information science with modern data management.

Before joining Actian, Ole served as Chief Evangelist at Zeenea, where he played a key role in shaping and communicating the company’s technology vision. His industry experience includes leadership roles in enterprise architecture and data strategy at major pharmaceutical companies like Novo Nordisk.Ole is passionate about scalable metadata architectures, knowledge graphs, and enabling organizations to make data truly discoverable and usable.

Connect with Ole online

LinkedIn

Substack

Medium

Resources mentioned in this interview

Fundamentals of Metadata Management, Ole's forthcoming book

Data Management at Scale by Piethein Strengholt

Fundamentals of Data Engineering by Joe Reis and Matt Housley

Meta Grid as a Team Topology, Substack article

Stewart Brand's Pace Layers

Video

Here’s the video version of our conversation:

https://youtu.be/t01IZoegKRI

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 28. Every modern enterprise wrestles with the scale, the complexity, and the urgency of understanding their data and metadata. So, by necessity, comprehensive architectural approaches like microservices and the data mesh are complex, big, and fast. Ole Olesen-Bagneux proposes a simple, small, and slow way for enterprises to cultivate a shared understanding of their enterprise knowledge, a decentralized approach to metadata strategy that he calls the Meta Grid.

Interview transcript

Larry:

Hi,

Andrea Volpini

Your organization's brand is what people say about you after you've left the room. It's the memories you create that determine how people think about you later.

Andrea Volpini says that the same dynamic applies in marketing to AI systems. Modern brand managers, he argues, need to understand how both human and machine memory work and then use that knowledge to create digital memories that align with how AI systems understand the world.

We talked about:

his work as CEO at WordLift, a company that builds knowledge graphs to help companies automate SEO and other marketing activities

a recent experiment he did during a talk at an AI conference that illustrates the ability of applications like Grok and ChatGPT to build and share information in real time

the role of memory in marketing to current AI architectures

his discovery of how the agentic approach he was taking to automating marketing tasks was actually creating valuable context for AI systems

the mechanisms of memory in AI systems and an analogy to human short- and long-term memory

the similarities he sees in how the human neocortex forms memories and how the knowledge about memory is represented in AI systems

his practice of representing entities as both triples and vectors in his knowledge graph

how he leverages his understanding of the differences in AI models in his work

the different types of memory frameworks to account for in both the consumption and creation of AI systems: semantic, episodic, and procedural

his new way of thinking about marketing: as a memory-creation process

the shift in focus that he thinks marketers need to make, "creating good memories for AI in order to protect their brand values"

Andrea's bio

Andrea Volpini is the CEO of WordLift and co-founder of Insideout10. With 25 years of experience in semantic web technologies, SEO, and artificial intelligence, he specializes in marketing strategies. He is a regular speaker at international conferences, including SXSW, TNW Conference, BrightonSEO, The Knowledge Graph Conference, G50, Connected Data and AI Festival.

Andrea has contributed to industry publications, including the Web Almanac by HTTP Archive. In 2013, he co-founded RedLink GmbH, a commercial spin-off focused on semantic content enrichment, natural language processing, and information extraction.

Connect with Andrea online

LinkedIn

X

Bluesky

WordLift

Video

Here’s the video version of our conversation:

https://youtu.be/do-Y7w47CZc

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 27. Some experts describe the marketing concept of branding as, What people say about you after you’ve left the room. It's the memories they form of your company that define your brand. Andrea Volpini sees this same dynamic unfolding as companies turn their attention to AI. To build a memorable brand online, modern marketers need to understand how both human and machine memory work and then focus on creating memories that align with how AI systems understand the world.

Interview transcript

Larry:

Hi, everyone. Welcome to episode number 27 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Andrea Volpini. Andrea is the CEO and the founder at WordLift, a company based in Rome. Tell the folks a little bit more about WordLift and what you're up to these days, Andrea.

Andrea:

Yep. So we build knowledge graphs and to help brands automate their SEO and marketing efforts using large language model and AI in general.

Larry:

Nice. Yeah, and you're pretty good at this. You've been doing this a while and you had a recent success story, I think that shows, that really highlights some of your current interests in your current work. Tell me about your talk in Milan and the little demonstration you did with that.

Andrea:

Yeah, yeah, so it was last week at AI Festival,

Jacobus Geluk

The arrival of AI agents creates urgency around the need to guide and govern them.

Drawing on his 15-year history in building reliable AI solutions for banks and other enterprises, Jacobus Geluk sees a standards-based data-product marketplace as the key to creating the thriving data economy that will enable AI agents to succeed at scale.

Jacobus launched the effort to create the DPROD data-product description specification, creating the supply side of the data market. He's now forming a working group to document the demand side, a "use-case tree" specification to articulate the business needs that data products address.

We talked about:

his work as CEO at Agnos.ai, an enterprise knowledge graph and AI consultancy

the working group he founded in 2023 which resulted in the DPROD specification to describe data products

an overview of the data-product marketplace and the data economy

the need to account for the demand side of the data marketplace

the intent of his current work on to address the disconnect between tech activities and business use cases

how the capabilities of LLMs and knowledge graphs complement each other

the origins of his "use-case tree" model in a huge banking enterprise knowledge graph he built ten years ago

how use case trees improve LLM-driven multi-agent architectures

some examples of the persona-driven, tech-agnostic solutions in agent architectures that use-case trees support

the importance of constraining LLM action with a control layer that governs agent activities, accounting for security, data sourcing, and issues like data lineage and provenance

the new Use Case Tree Work Group he is forming

the paradox in the semantic technology industry now of a lack of standards in a field with its roots in W3C standards

Jacobus' bio

Jacobus Geluk is a Dutch Semantic Technology Architect and CEO of agnos.ai, a UK-based consulting firm with a global team of experts specializing in GraphAI — the combination of Enterprise Knowledge Graphs (EKG) with Generative AI (GenAI). Jacobus has over 20 years of experience in data management and semantic technologies, previously serving as a Senior Data Architect at Bloomberg and Fellow Architect at BNY Mellon, where he led the first large-scale production EKG in the financial industry.

As a founding member and current co-chair of the Enterprise Knowledge Graph Forum (EKGF), Jacobus initiated the Data Product Workgroup, which developed the Data Product Ontology (DPROD) — a proposed OMG standard for consistent data product management across platforms. Jacobus can claim to have coined the term "Enterprise Knowledge Graph (EKG)" more than 10 years ago, and his work has been instrumental in advancing semantic technologies in financial services and other information-intensive industries.

Connect with Jacobus online

LinkedIn

Agnos.ai

Resources mentioned in this podcast

DPROD specification

Enterprise Knowledge Graph Forum

Object Management Group

Use Case Tree Method for Business Capabilities

DCAT Data Catalog Vocabulary

Video

Here’s the video version of our conversation:

https://youtu.be/J0JXkvizxGo

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 26. In an AI landscape that will soon include huge groups of independent software agents acting on behalf of humans, we'll need solid mechanisms to guide the actions of those agents. Jacobus Geluk looks at this situation from the perspective of the data economy, specifically the data-products marketplace. He helped develop the DPROD specification that describes data products and is now focused on developing use-case trees that describe the business needs that they address.

Interview transcript

Larry:

Okay. Hi everyone. Welcome to episode number 26 of the Knowledge Graph Insights podcast. I am really happy today to welcome to the show, Jacobus Geluk. Sorry, I try to speak Dutch, do my best.

Rebecca Schneider

Skills that Rebecca Schneider learned in library science school - taxonomy, ontology, and semantic modeling - have only become more valuable with the arrival of AI technologies like LLMs and the growing interest in knowledge graphs.

Two things have stayed constant across her library and enterprise content strategy work: organizational rigor and the need to always focus on people and their needs.

We talked about:

her work as Co-Founder and Executive Director at AvenueCX, an enterprise content strategy consultancy

her background as a "recovering librarian" and her focus on taxonomies, metadata, and structured content

the importance of structured content in LLMs and other AI applications

how she balances the capabilities of AI architectures and the needs of the humans that contribute to them

the need to disambiguate the terms that describe the span of the semantic spectrum

the crucial role of organization in her work and how you don't to have formally studied library science to do it

the role of a service mentality in knowledge graph work

how she measures the efficiency and other benefits of well-organized information

how domain modeling and content modeling work together in her work

her tech-agnostic approach to consulting

the role of metadata strategy into her work

how new AI tools permit easier content tagging and better governance

the importance of "knowing your collection," not becoming a true subject matter expert but at least getting familiar with the content you are working with

the need to clean up your content and data to build successful AI applications

Rebecca's bio

Rebecca is co-founder of AvenueCX, an enterprise content strategy consultancy. Her areas of expertise include content strategy, taxonomy development, and structured content. She has guided content strategy in a variety of industries: automotive, semiconductors, telecommunications, retail, and financial services.

Connect with Rebecca online

LinkedIn

email: rschneider at avenuecx dot com

Video

Here’s the video version of our conversation:

https://youtu.be/ex8Z7aXmR0o

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 25. If you've ever visited the reference desk at your local library, you've seen the service mentality that librarians bring to their work. Rebecca Schneider brings that same sensibility to her content and knowledge graph consulting. Like all digital practitioners, her projects now include a lot more AI, but her work remains grounded in the fundamentals she learned studying library science: organizational rigor and a focus on people and their needs.

Interview transcript

Larry:

Hi, everyone. Welcome to episode number 25 of the Knowledge Graph Insights podcast. I am really excited today to welcome to the show Rebecca Schneider. Rebecca is the co-founder and the executive director at AvenueCX, a consultancy in the Boston area. Welcome, Rebecca. Tell the folks a little bit more about what you're up to these days.

Rebecca:

Hi, Larry. Thanks for having me on your show. Hello, everyone. My name is Rebecca Schneider. I am a recovering librarian. I was a trained librarian, worked in a library with actual books, but for most of my career, I have been focusing on enterprise content strategy. Furthermore, I typically focus on taxonomies, metadata, structured content, and all of that wonderful world that we live in.

Larry:

Yeah, and we both come out of that content background and have sort of converged on the knowledge graph background together kind of over the same time period. And it's really interesting, like those skills that you mentioned, the library science skills of taxonomy, metadata, structured, and then the application of that in structured content in the content world, how, as you've got in more and more into knowledge graph stuff, how has that background, I guess...

Ashleigh Faith

With her 15-year history in the knowledge graph industry and her popular YouTube channel, Ashleigh Faith has informed and inspired a generation of graph practitioners and enthusiasts.

She's an expert on semantic modeling, knowledge graph construction, and AI architectures and talks about those concepts in ways that resonate both with her colleagues and with newcomers to the field.

We talked about:

her popular IsA DataThing YouTube channel

the crucial role of accurately modeling actual facts in semantic practice and AI architectures

her appreciation of the role of knowledge graphs in aligning people in large organizations around concepts and the various words that describe them

the importance of staying focused on the business case for knowledge graph work, which has become both more important with the arrival of LLMs and generative AI

the emergence of more intuitive "talk to your graph" interfaces

some of her checklist items for onboarding aspiring knowledge graph engineers

how to decide whether to use a property graph or a knowledge graph, or both

her hope that more RDF graph vendors will offer a free tier so that people can more easily experiment with them

approaches to AI architecture orchestration

the enduring importance of understanding how information retrieval works

Ashleigh's bio

Ashleigh Faith has her PhD in Advanced Semantics and over 15 years of experience working on graph solutions across the STEM, government, and finance industries. Outside of her day-job, she is the Founder and host of the IsA DataThing YouTube channel and podcast where she tries to demystify the graph space.

Connect with Ashleigh online

LinkedIn

IsA DataThing YouTube channel

Video

Here’s the video version of our conversation:

https://youtu.be/eMqLydDu6oY

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 24. One way to understand the entity resolution capabilities of knowledge graphs is to picture on old-fashioned telephone operator moving plugs around a switchboard to make the right connections. Early in her career, that's one way that Ashleigh Faith saw the power of knowledge graphs. She has since developed sophisticated approaches to knowledge graph construction, semantic modeling, and AI architectures and shares her deeply informed insights on her popular YouTube channel.

Interview transcript

Larry:

Hi, everyone. Welcome to episode number 24 of the Knowledge Graph Insights Podcast. I am super extra delighted today to welcome to the show Ashleigh Faith. Ashleigh is the host of the awesome YouTube channel IsA DataThing, which has thousands of subscribers, thousands of monthly views. I think it's many people's entry point into the knowledge graph world. Welcome, Ashleigh. Great to have you here. Tell the folks a little bit more about what you're up to these days.

Ashleigh:

Thanks, Larry. I've known you for quite some time. I'm really excited to be here today.

What about me? I do a lot of semantic and AI stuff for my day job. But yeah, I think my main passion is also helping others get involved, understand some of the concepts a little bit better for the semantic space and now the neuro-symbolic AI. That's AI and knowledge graphs coming together. That is quite a hot topic right now, so lots and lots of untapped potential in what we can talk about. I do most of that on my channel.

Larry:

Yeah. I will refer people to your channel because we've got only a half-hour today. It's ridiculous.

Ashleigh:

Yeah.

Larry:

We just talked for an hour before we went on the air. It's ridiculous. What I'd really like to focus on today is the first stage in any of this, the first step in any of these knowledge graph implementations or any of this stuff is modeling. I think about it from a designerly perspective. I do a lot of mental model discernment, user research kind of stuff, and then conceptual modeling to agree on things.

Panos Alexopoulos

Any knowledge graph or other semantic artifact must be modeled before it's built.

Panos Alexopoulos has been building semantic models since 2006. In 2020, O'Reilly published his book on the subject, "Semantic Modeling for Data."

The book covers the craft of semantic data modeling, the pitfalls practitioners are likely to encounter, and the dilemmas they'll need to overcome.

We talked about:

his work as Head of Ontology at Textkernel and his 18-year history working with symbolic AI and semantic modeling

his definition and description of the practice of semantic modeling and its three main characteristics: accuracy, explicitness, and agreement

the variety of artifacts that can result from semantic modeling: database schemas, taxonomies, hierarchies, glossaries, thesauri, ontologies, etc.

the difference between identifying entities with human understandable descriptions in symbolic AI and numerical encodings in sub-symbolic AI

the role of semantic modeling in RAG and other hybrid AI architectures

a brief overview of data modeling as a practice

how LLMs fit into semantic modeling: as sources of information to populate a knowledge graph, as coding assistants, and in entity and relation extraction

other techniques besides NLP and LLMs that he uses in his modeling practice: syntactic patterns, heuristics, regular expressions, etc.

the role of semantic modeling and symbolic AI in emerging hybrid AI architectures

the importance of defining the notion of "autonomy" as AI agents emerge

Panos' bio

Panos Alexopoulos has been working since 2006 at the intersection of data, semantics and software, contributing in building intelligent systems that deliver value to business and society. Born and raised in Athens, Greece, Panos currently works as a principal educator at OWLTECH, developing and delivering training workshops that provide actionable knowledge and insights for data and AI practitioners. He also works as Head of Ontology at Textkernel BV, in Amsterdam, Netherlands, leading a team of data professionals in developing and delivering a large cross-lingual Knowledge Graph in the HR and Recruitment domain. Panos has published several papers at international conferences, journals and books, and he is a regular speaker in both academic and industry venues. He is also the author of the O’Reilly book “Semantic Modeling for Data – Avoiding Pitfalls and Dilemmas”, a practical and pragmatic field guide for data practitioners that want to learn how semantic data modeling is applied in the real world.

Connect with Panos online

LinkedIn

Video

Here’s the video version of our conversation:

https://youtu.be/ENothdlfYGA

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 23. In order to build a knowledge graph or any other semantic artifact, you first need to model the concepts you're working with, and that model needs to be accurate, to explicitly represent all of the ideas you're working with, and to capture human agreements about them. Panos Alexopoulos literally wrote the book on semantic modeling for data, covering both the principles of modeling as well as the pragmatic concerns of real-world modelers.

Interview transcript

Larry:

Hi everyone. Welcome to episode number 23 of the Knowledge Graph Insights podcast. I am really excited today to welcome to the show Panos Alexopoulos. Panos is the head of ontology at Textkernel, a company in Amsterdam that works on knowledge graphs for the HR and recruitment world. Welcome, Panos. Tell the folks a little bit more about what you're doing these days.

Panos:

Hi Larry. Thank you very much for inviting me to your podcast. I'm really happy to be here. Yeah, so as you said, I'm head of ontology at Textkernel. Actually, I've been working in the field of data semantics, knowledge graph ontologies for almost now 18 years, even before the era of machine learning,