Alan Morrison

After 20-plus years of industry analysis, Alan Morrison has developed a keen sense for how knowledge graphs can help enterprises.

Even though he has focused on advanced tech and emerging IT practices and is deeply immersed and invested in current tech developments, much of his advice for enterprises looking to develop their data maturity involves pragmatic baby steps and basic mindset shifts.

We talked about:

his work in the consulting world and his organizing work around the knowledge graph community to improve awareness of the technology

the need to find "foxes instead of the hedgehogs" in enterprises when you're trying to promote adoption of new tech

the relationships between different AI tech, like LLMs and knowledge graphs, and the common connection they share: data

the importance of having mature data practices in any enterprise

how even simple metadata practices in common tools like spreadsheets can support better enterprise data practices

how sidestepping the formal org chart and forming guerrilla teams can advance data practice

the benefits of starting small in any knowledge graph project

how representing organization knowledge at a high level in a knowledge graph can help solve big enterprise problems

how a knowledge graph gives you a multidimensional Tinker Toys set to model and understand your org's data

the benefits of moving from tabular thinking to graph thinking

his frustration with the current framing of AI as being solely about machine learning

his observation that practices across any org - content, knowledge management, data management, business people - could benefit from long-standing standards and proven technologies (that might not be as sexy and topical as LLMs)

Alan's bio

Alan Morrison is a longtime analyst, writer, advisor and podcaster on advanced data technologies and emerging IT. For 20 years at PwC's R&D and innovation think tanks, Alan identified emerging technologies on the cusp of adoption, assessed their business impacts, and advised PwC's clients on innovation strategy.

Before PwC, he was a semiconductor industry market analyst and forecaster, a retail site location analyst, and a US Navy intelligence analyst, Russian linguist and aircrewman.

For the last five years, Alan has been a contributor on knowledge graph and related topics for Data Science Central. His writings over the years have covered dozens of different technologies.

Connect with Alan online

LinkedIn

Video

Here’s the video version of our conversation:

https://youtu.be/TXOWWjM-DBc

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 5. You might think that the lofty perch of multiple decades in industry-analyst roles would inspire grand visions of tech transformation with leading-edge technology. Quite the opposite in the case Alan Morrison. He shows how enterprises can advance their data maturity by cultivating basic graph thinking in their organizations and by taking small, pragmatic steps like adopting established standards for interoperability or simply adding metadata to a spreadsheet.

Interview transcript

Larry:

Hi, everyone. Welcome to episode number five of the Knowledge Graph Insights podcast. I am really happy today to welcome to the show Alan Morrison. Currently, he's a contributor at Data Science Central, a well-known publication in the field. He's a freelancer and consultant around knowledge graphs and a lot of other areas as well. His background, he comes out of the consulting world. Most recently before his current role as a freelancer and consultant, he worked for many years at PriceWaterhouseCooper, the big consultancy as a senior research fellow. So welcome, Alan. Tell the folks a little bit more about what you're up to these days.

Alan:

Hey, Larry. Great to talk with you and folks should know that you and I have some history together as a part of the Data Worthy Collective, which is like an informal meetup group, collaborative thinking going on every week, and it's been great to know you over the years. What I'm doing currently is trying to help the knowledge graph community, in particular, gain more visibility, gain more traction in the enterprise, and it's a long haul. Enterprises are slow to change. It's like turning an oil tanker on a dime. It's very, very hard kind of thing to do, and there's so much legacy involved. And so when I was at PWC, we worked with a lot of large companies and you'd look for pockets of innovation and you'd look for the foxes instead of the hedgehogs, because the foxes were the ones that were curious about doing things different ways. The hedgehogs were the ones who were expert in doing things in one way.

Alan:

So we had this kind of guerrilla approach to innovation, and we also worked with the centralized innovation group inside the firm to try to help the firm itself modernize. And so in my last five years at the firm, I was plugged into the AI efforts that were emerging because machine learning was becoming much more feasible. And so I've taken this knowledge that I have of AI and the semantic web so-called, an old term, but it still has utility, together, and I'm just trying to help enterprises see the advantages of these things and adopt them to the extent they can be.

Larry:

You just said how hard, notoriously difficult it is to get enterprises to think and act differently, but they're all jumping all over AI like it's the best thing since sliced bread. But there's a lot of opportunities there, it seems like, to not ride the coattails, but to enter the conversation around these new technologies. And there's a lot of interplay between generative AI and machine learning and LLMs and all that world and the knowledge graph world. Can you kind of stitch those worlds together for us a little bit?

Alan:

Yeah. I think it's good to do that. It's good to step back and say, "What are we trying to do here? How are we trying to do it?" We've got some piece parts that are talked about in the media at infinitum. Generative AI is just all the time in the conversation because it's a powerful interface technology, as it stands, and some enterprise providers are using GAI as basically a front end, and then they will connect their own backend. And so I think you have to think about generative AI and other kinds of AI in the context of this bigger picture, and it's all driven by data. And when we say data, we don't just mean binary bits, I think we mean ideally contextualized information, knowledge, getting wisdom and decision-making capability to the right point where it's actionable at the right time for the right purpose.

Alan:

And so it's a distribution problem of knowledge, essentially the right kinds of knowledge. And so you really have to think about data, and this is where I get passionate about it, because the information, the heart of it is in this contextualized environment that should be being built, and it should be an organic kind of effort that involves both humans and machines. And I'm a woodworker, so I think about machine learning as a kind of a table saw. And so the knowledge is the wood that you're working with, it's an organic thing. And so you're using all this different kinds of tooling. I got a lot of different kinds of tooling in my workshop, and I'm not a great woodworker, but I think that the source of the wood is really important, what kind of wood you're working with. And we could do all sorts of things if we had the right resources inside of enterprises.

Alan:

Enterprises are really starved for good data. They just are not in the habit of collecting it very well. I was in intelligence in the Navy when I started, just collecting voice traffic and analyzing it. And it was so systematic about how the data collection happened. There was this whole data lifecycle environment that we were a part of, and everybody was managing according to the needs of that. And I just think that that was a very effective way that enterprises could take advantage of to really collect what they need to and just understand if you're going to digitize things, you have to have this continual process of collecting and analyzing and managing this information. And it has to be organically constructed so that it's scalable and it does what you need it to do. So that's basically where I've been focused over the past years.

Larry:

Yeah. The way you described that is so evocative of, I love the word working analogy, but also your military experience. I think any enterprise would argue that they would claim to attribute the same importance or similar level of importance as naval intelligence data about whatever you were researching. And yet, they have these sloppy data practices, or if not sloppy, at least not thought-through and sort of suboptimal. Can you talk about two things? One, how could they be better at that data hygiene and that data practice that you just described that was so well entrenched in your Navy days? And then in particular, how knowledge graphs and the whole world of semantic tech can help you do more with that data, and is it a prerequisite to have that good data hygiene before you can do the cool stuff with knowledge graphs?

Alan:

Let me start with the last question first. It is a prerequisite to have a certain amount of data maturity. When I was at PWC, we had a data maturity curve, and it was frustrating to see that most of our audit clients were not terribly high on that maturity curve. I think the tooling gets in the way. There's so much siloing that has gone on over the decades, and so many folks, including me, are in the habit of just using certain tools. And so the learning curve for learning something new is a bit steep.

Ellie Young

Ellie Young effortlessly connects the human and technical elements that go into ontologies and knowledge graph building.

Ellie came to the world of knowledge graphs with backgrounds in both literature and sustainability. "If the world wasn't on fire," she says, " I would probably be writing novels."

That sense of urgency drives her work at Common Action, a platform she is creating to address climate change and advance sustainability. She also applies her knowledge graph expertise in projects like HelioWeb at NASA, which connects scientists in the field of heliophysics.

We talked about:

her work at Common Action, a platform for climate and for sustainability that uses knowledge graph technology

her work at NASA to facilitate collaboration and expose knowledge across the domain of heliophysics (the study of the sun)

how personal knowledge graphs can connect individuals and collectives of people

how her background in design, art,literature, and the humanities manifests in her knowledge graph work

her desire to leverage metadata and capabilities like her language knowledge to facilitate topical discovery

the interplay between the efficiencies that AI tech like LLMs offer and the uniquely imaginative variations that human beings create

the importance of the practice of design in advancing the productive use of information

how user experience design connects to ontologies and back-end tech

how she applies, and imagines how others might apply, a literary mindset to ontology practice

how she applied ethnographic methods from anthropology to a paper she co-authored on the NASA HelioWeb ontology

her ongoing call for volunteers to help with her Common Action program, specifically a current need for creating a "phenomena ontology"

Ellie's bio

Ellie Young brings knowledge to communities to catalyze successful, local actions to address climate/sustainability problems.

She is the founder of Common Action, an innovation network facilitating climate and sustainability action through the development of community and knowledge graph technology.

Previously she served as Head of Community and Director of Conference Operations at The Knowledge Graph Conference.

Connect with Ellie online

LinkedIn

ellie at common-action dot org

Resources

The cultural-social nucleus of an open community: A multi-level community knowledge graph and NASA application, Applied Computing and Geosciences

Common Action vision

Video

Here’s the video version of our conversation:

https://youtu.be/CL9HWocoh7I

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 4. In the domains of ontology engineering and the semantic web, there are plenty of people with advanced technical skills. Practitioners with social-science skills, well-developed literary instincts, and a design mindset are harder to find. Ellie Young smoothly navigates the technical and linguistic worlds that intersect in knowledge graphs, applying her humanities mindset to projects that connect scientists at NASA and address climate change and sustainability.

Interview transcript

Larry:

Okay. Hi everyone. Welcome to episode number four of the Knowledge Graph Insights podcast. I am super delighted today to welcome to the show Ellie Young. Ellie is the founder of Common Action, a sustainability and climate change activist organization. She's also ... The way I first met her, she's the former head of community for the Knowledge Graph Conference, and one of the people who really ushered a lot of people into this community. So welcome Ellie. Tell the folks a little bit more about what you're up to these days.

Ellie:

Thanks, Larry, and thank you for inviting me to be a special guest with you on this very nice community visit. So yeah. Now I've gone from KGC. I think I left about three years ago to starting Common Action, which is not really an activist organization. It's more like of a design and deep operations platform. And so what we're trying to do is bring technology to the climate problem. Because if we think about it, there's many, many things that make climate challenging to solve. But one of the core components is that it's a coordination challenge. It's a communication challenge. It's a really big project. And so if we think about really big projects, those tend to happen on Gmail or on Slack or maybe on Teams if you're really unlucky and it's a lot of interaction between moving parts, many people, and there's a lot of confusion in that. And it's always dependent on those people knowing each other, especially when you think about project management software like Asana or something like that.

Ellie:

So in the climate case where we literally cannot know everybody who needs to do something about climate because it's the globe, how would we think about organizing those communications and supporting specific people to identify routes to action pathways that help them become part of this mission set in a productive way? So anybody who wants to do something about climate, where do they go? What do they do? We are trying to be that middle space, both for just equipping people with strategies and also for looking at the unfolding changes globally in the physical layer and anticipating what kind of effects we might have and then supporting people to respond and prepare to those as well. So there's a whole lot of things that we want to facilitate, but at the end of the day, it's a technology-based firm that is bringing to the market complexity-based software, and that is always backed by knowledge graph technology.

Larry:

Because how else would you ever organize that scope that you just described? That's a really ambitious domain you described. But your current work, you're working in a slightly more constrained domain. At NASA, you're working on the HelioWeb project. That's at least ... What? Science is the boundary of that one?

Ellie:

Yeah. Well, even better, it's one division of science. So we're just cutting our teeth on a small project for NASA. And what this is called ... And as you said, it's called HelioWeb. What this is about is basically solving some of the same communication and discovery problems for scientists who work both within NASA and beyond NASA within this investigation space of the heliophysics. And so you probably haven't heard of heliophysics. It's not as well socialized as astrophysics, but it is actually another form of astrophysics and it's all about the study of the sun. So there's a lot of people that participate in studying the sun, not only inside NASA, as I mentioned, but also in universities and also abroad in the European Space Agency and other related space agencies.

Ellie:

As I mentioned, what are our collaboration opportunities? Well, we can work on Slack, we can go to Teams and we can meet in a conference. And so what we're trying to do is expose the knowledge that is latent in a conference space. And we've talked about this too at KGC. So that we can see who is there beyond a simple interaction in the hallway. Because let's say you go to a conference, there's a thousand people there, you can't talk to every one of them. And even if you did, you wouldn't necessarily know everything that would be relevant for them. For them to tell you or for you to share with them. We just don't have the surface area for that in our interactions in person. So what we're doing is supplementing that with a software system that allows individuals who are part of the HelioWeb and heliophysics community to report items of interest about themselves. That could be activities that they're doing, roles that they have, projects that they're organizing, softwares that they built or are maintaining. Any of the contributions that they make to science they can record it in a catalog, and then they can search using an ontology for different kinds of parameters, which are not connected necessarily to an individual name.

Ellie:

So I may not know who I would like to work with in this group of thousands of scientists because I haven't met them yet, or because I don't know how someone's research interests have transformed or morphed over time. But I know that I'm interested in solar flares or coronal mass eruptions. And so I can search for those topics and see what other people have contributed to the knowledge base on those topics like, like I said, software systems or data sets or papers or whatever you have that people have entered. And then so in that way, discover the actual network. So it's this user interface navigation notebook for supporting the discourse in the field alongside making it accessible to find materials, which is also a challenge in the NASA environment. As you can imagine, they have millions, trillions of data objects.

Larry:

No kidding. As you're saying that, you're reminding me, have you ever talked to Ashley Faith about personal knowledge graphs?

Ellie:

A little bit.

Larry:

Yeah. Because as you're saying that, one of the things ... Because I was talking to her on another podcast years ago about this notion of personal knowledge graphs as a way to just know and understand people, but we were talking about them in the context of enterprise content strategy as a way to know people who know stuff and to get permission to share stuff. But it was all about sharing the kinds of things you just mentioned, like personal interests, things you've done, activities you've done. So the way you described that, you can totally see how you just associate in the ontology, all those interests and activities with the people. Is it as simple as that, or is there more to it? Were there any challenges I guess, in developing the ontology that drives that?

Ellie:

Yeah. There's multiple stages of HelioWeb, and in the current moment we're in the first stage, which is this catalog.

George Anadiotis

Every profession has its connectors, sharers, and community organizers. In the knowledge graph world, George Anadiotis fills all of these roles.

Through his industry analysis and reporting, his conference organizing, and his writing and podcasting, George connects ideas and people across the semantic-tech landscape.

We talked about:

his work at Linked Data Orchestration and as a consultant and analyst in the knowledge graph and linked-data world

his diverse background in computing and his studies at the intersection of knowledge management, the semantic web, and distributed systems

his extensive writing experience and consulting background

his definition of a knowledge graph

the differences between RDF-based knowledge graphs and labeled property graphs (LPG)

the focus in the RDF community on standards and interoperability versus the focus in the LPG community on implementation

the variety of query languages in the LPG world and recent efforts like GQL to create a standard way of querying LPGs, as well as efforts to query across both RDF and LPG graphs

the origins of his annual Year of the Graph report

some of the reasons that knowledge graphs are positioned in the bullseye of Gartner's Impact Radar this year

where knowledge graphs fit in the AI landscape

the role of knowledge graphs in RAG architectures

the conference he organizes, Connected Data London, coming up December 11-13

George's bio

George Anadiotis has got tech, data, AI and media, and he's not afraid to use them.

He helps organizations map and understand complex domains to make better decisions; design, implement and monitor models, processes and systems to achieve goals; and craft communication strategies and outreach initiatives to grow awareness and market share.

He enjoys researching, developing, applying, writing and talking about cutting edge concepts and technology, and their implications on society and business.

Connect with George online

LinkedIn

Twitter

TikTok

Instagram

George's publications, podcasts, and conference

Connected Data London (conference roundtable recording)

The Year of the Graph

Orchestrate All the Things

Video

Here’s the video version of our conversation:

https://youtu.be/lEHj5_9y-30

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 3. In any domain, there are people who seem to do it all - practice and consultation, industry analysis and reporting, and community building and event organizing. In the world of knowledge graphs and the semantic web, George Anadiotis has filled all of these roles. Whether he's publishing his Year of the Graph newsletter, organizing the annual Connected Data conference, or producing the latest Orchestrate All the Things podcast, George is always connecting the dots.

Interview transcript

Larry:

Hi, everyone. Welcome to episode number three of the Knowledge Graph Insights Contest. Sorry, I'm going to redo that again. I have too many podcasts. I need a new intro for this one. Okay. Hi, everyone. Welcome to episode number three of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show George Anadiotis. George is really well known in the knowledge graph world and the graph world in general and the tech world in general, as an analyst, a consultant, a really well-developed engineer. He runs a big conference around knowledge graph and graph technology, and he is the principal at his organization called Linked Data Orchestration. So welcome, George, tell the folks a little bit more about what you're up to these days.

George:

Great. Thanks for the intro, Larry, and good to be here. Actually one of the opening, I guess, guests for this new podcast series of yours. Well, the truth is I have a long and kind of convoluted story, but I've kind of honed my skills of telling it in as simple way as possible. So basically in terms of background, I have a very hardcore computer science background. I was one of those kids that I saw my first computer when I was like 12, and immediately I kind of snapped and I realized, "Okay, so this is what I'm going to do in life." So went to college, studied computer science, graduated, started working as a software engineer and architect and all of that stuff, consultant, all of that. And then at some point, about a decade in basically, I realized that it's been fun, but I wanted to try something new.

George:

And that's the point where graphs sort of entered my life because the thing... I was interested in research and my topic was somewhere around the intersection of knowledge management, semantic web and distributed systems, and there was a specific group that I wanted to join that was working precisely on the intersection of those things, the Knowledge Representation and Reasoning group based in Amsterdam, led by one of my mentors, Frank Van Harmerlen. So I was lucky enough to spend a few years there, did some really cool stuff in that group up until the point where I left, I repatriated. So I should also mention I'm from Greece originally, so I spent a few years in Amsterdam, then moved back to Greece, kept working on the intersection of those technologies actually. But for a few years I did that leading the R&D of a company that was developing projects and products around those up until 2012.

George:

And that's the point where I started doing my solopreneur thing. So ever since I've been juggling a few things. So I work as an analyst, I collaborate with GigaOm, I work as a writer. I've contributed to a few publications such as VentureBeat and ZDNET. I have my own newsletter and blog called the Orchestrate all the Things podcast and newsletter. What else? Let's see. As you mentioned, I organize an event, it's called Connected Data. I think we can elaborate a little bit on that later because it's actually very much relevant for the knowledge graph theme. I also curate a newsletter called The Year of the Graph and their graph database report also going by the same name to consulting with a number of companies from... Pretty much everything ranging from go-to-market strategy, marketing to technical implementation. So I juggle many balls, as I said.

Larry:

I'm exhausted just listening to that and I feel very grateful that you found the time to talk to me with all that you have going on. So thank you, George. Hey, one thing I like to start each episode with is I would love to get your definition of a knowledge graph just for folks... The idea is to hopefully come up with something like a canonical definition somewhere in the next one to five years. But anyhow, I'd love to get your take on what a knowledge graph is.

George:

Yeah, that's a good one. And somehow it never gets old. I'm sure you're probably familiar with the fact that I believe a couple of years when I last checked, I think there were over 100 definitions for what constitutes a knowledge graph. So they've probably grown to, I don't know, maybe 200 by now. So 200, 201 who's counting. I'm going to give you mine as well. And by the way, if you ask me next year, I'm probably going to tell you something slightly different, but here's the current definition. So if we're talking about the graph, then it basically means that we're talking about the data model in which the key elements are nodes and edges.

George:

I'm going to add the directed adjective to edges because well, if you only have edges without direction, it may lead to some ambiguity, let's say. So that's the graph part, which it's not very original, it's kind of the textbook definition. What may be a bit more original is the knowledge part. So I think in order to be able to qualify a graph as being a knowledge graph, I think there are certain conditions that need to be met. So basically I think that both nodes and edges should enable users to define their properties and they should adhere to a schema. That's as lightweight as I could possibly keep it without getting too technical.

Larry:

Interesting. And that notion of a schema and both having properties on the edges. I guess maybe want to diverge just a little bit and talk about the difference between an RDF-based, triple-based knowledge graph and a labeled property graph. Can you talk a little bit... That might be another thing to really get... Because I think a lot of people, when they hear graph technology, they're thinking most... I think the most common databases and tools that are out there are often around labeled property graphs. So can you help us tease out between an RDF-based knowledge graph and a labeled property graph?

George:

Okay, well, we could spend at least four or five podcast episodes talking just about that. And by the way, there recently was another episode by a good friend actually, and also very knowledgeable person in the graph world, Amy Hodler. So she spent an entire episode with her guests dissecting this exact topic. So what's an RDF-knowledge graph? What's a labeled property graph? How are they different? When should I use what and so on. So I'm not going to even try and be as extensive as they were, but let me just say that for most people, if they're not familiar with graphs at all, maybe just the general idea of the graph data model, let's say, they don't even know... They can't actually imagine, I'm guessing, that in the graph world you do have this kind of schism.

George:

So there's two ways of modeling graphs, because if you think about it, that's not the case for relational data. As far as I can remember, that's not the case for the document data model either. So in those words, things are pretty straightforward. Okay, so I want to build a relational database. I have tables, I have SQL. There's one way to go, basically. Yes, you may have slight variations on the query languages, but for the most part it's all pretty standard and pretty straightforward.

Mike Dillinger

Knowledge graphs provide the digital foundation for some of most visible companies on the web.

Mike Dillinger built LinkedIn's Economic Graph, the knowledge graph that powers the social media giant's recommendation systems.

Mike now helps people understand knowledge graph technology and how it can complement and improve generative AI, whether by acting as "jet fuel" to better train LLMs or by providing "adult supervision" for their unruly, adolescent behavior.

We talked about:

how he describes knowledge graphs

how the richness of information in a knowledge graph helps computers better understand the things in a system

the differences between knowledge graphs and LLMs

how LinkedIn's Economic Graph, which Mike's team built, works

how LLMs can help build knowledge graphs, and how knowledge graphs can act as "jet fuel" to train LLMs

the RDF "triples" that are at the foundation of knowledge graphs

the importance of distinguishing between unique concepts in a knowledge graph and how practitioners do this

the two main crafts needed to build knowledge graphs: linguistic expertise and software engineering

the job opportunities for language professionals in the LLM and knowledge graph worlds

the propensity of tech companies to staff knowledge graph efforts with engineers while there is actually a need for a variety of talent, as well as better collaboration skills

his assertion that "language professionals aren't janitors," put on teams only to clean up data for software engineers

how knowledge graphs provide "adult supervision" for unruly, adolescent LLMs

his hypothesis that using KGs as a separate modality of data rather than as training data for LLMs will advance AI

Mike's bio

Mike Dillinger, PhD is a technical advisor, consultant, and thought leader who champions the importance of capturing and leveraging reusable, explicit human knowledge to enable more reliable machine intelligence. He was Technical Lead for Knowledge Graphs in the AI Division at LinkedIn and for LinkedIn’s and eBay’s first machine translation systems. He was also an independent consultant specialized in deploying translation technologies for Fortune 500 companies, and Director of Linguistics at two machine translation software companies where he led development of the first commercial MT-TM integration. He was President of the Association for Machine Translation in the Americas and has two MT-related patents. Dr. Dillinger has also taught at more than a dozen universities in several countries, has been a visiting researcher on four continents, and has a weekly blog on Knowledge Architecture.

Connect with Mike online

LinkedIn

Video

Here’s the video version of our conversation:

https://youtu.be/wX2C3DwiWG4

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 2. If you've ever looked for a job or recruited talent on LinkedIn, you've seen Mike Dillinger's work. His team built LinkedIn's Economic Graph, the knowledge graph that powers the social media platform's recommendation system. These days, Mike thinks a lot about how knowledge graph technology can work with generative AI, seeing opportunities for the technologies to help the other, like the ability of knowledge graphs to act as "jet fuel" to train large language models.

Interview transcript

Larry:

Hi everyone. Welcome to Episode Number 2 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the program Mike Dillinger. Mike is a cage-free consultant based in San Jose. He's been doing knowledge graph and other technical things for many years. So welcome, Mike. Tell the folks a little bit more about what you're up to these days.

Mike:

Thanks a lot, Larry. What am I up to? I'm trying to help people wrap their heads around knowledge graphs and how we can make AI, transform GenAI into Next-GenAI by leveraging more explicit content and human knowledge.

Larry:

Ooh, I love that. Next-GenAI. I hope you've copyrighted that.

Mike:

No.

Larry:

Yeah, no, but that's ... because right now, it seems like most of the oxygen in the room has been sucked up by Open AI and LLMs and chatbots and GPTs, but knowledge graphs have something to offer as well. I guess the first thing I'd like to ask is, can you describe for folks, because I think a lot of folks listening to this podcast might not be as familiar as some of us with what a knowledge graph is and what it does. Can you sort of set out for folks what a knowledge graph is?

Mike:

Sure. The usual way I describe knowledge graphs are as collections of densely interconnected facts about individual things and categories of things, based on a range of different relations. So one thing that people get caught on is, oh, so there is a taxonomy? Not really, but taxonomies are a part of the knowledge graph. Oh, so it's an ontology? No, but ontologies are a part of the knowledge graph. So there are a range of different kinds of facts in a knowledge graph, so it's broader than an taxonomy or an ontology.

Larry:

And I think a lot of people come ... like, I work in the content world mostly, and information architecture. Most people in that world, I think the first time they go to organize stuff, they start thinking taxonomically, which I guess makes sense. But tell me the benefits of going beyond a simple taxonomy or just an ontology. How does it come together? How does it help people do more interesting and better stuff?

Mike:

Well, the question that we're talking about here is, you might call it the richness or the depth of the knowledge representation. With a taxonomy, you only have relations like this is a subcategory of that, or this is an instance of that, and you don't have information about what is this for or what are its attributes or what are its components? So when you move from a taxonomy to a knowledge graph, we're talking about giving algorithms more information in more detail about the things that we want them to think about.

Larry:

Interesting. And I think a lot of people right now, a lot of the curiosity and interest around these kinds of technologies is around LLMs and OpenAI and ChatGPT and all those things. Can you contrast a knowledge graph and what it can do with what those kinds of systems are doing?

Mike:

Oh, sure. So language models focus very much on strings, and sequences of strings, and knowledge graphs focus more on facts or concepts. So concepts built into facts, as it were. So they're really focusing on very different things: sequences of words or strings, or graphs of concepts. So the notion of meaning is really different. They're both in terms of similarity, but an LLM computes similar meaning in terms of context.

Mike:

So if two words have similar words around them, then those two words are considered in an LLM to have similar meanings. But in a knowledge graph, you compare to concepts by saying, oh, do they have similar components and similar characteristics? If so, then they're related in meaning.

Mike:

So they're very different ways of getting at a similar problem.

Larry:

Interesting.

Mike:

You might phrase it, for linguistic people, you might say LLMs focus on syntax and knowledge graphs focus on semantics, or LLMs focus on data and knowledge graphs focus on knowledge. There are a lot of different ways of describing it. So they're very, very much complementary technologies for getting at some of the same problems.

Larry:

You know what I'd love to do now is I'd love to ground what you just said in some examples. Like, what are your ... the first accomplishment of yours that I learned about was your work on the Economic Graph at LinkedIn. Can you talk a little bit about what the aim of that is and how a knowledge graph helped LinkedIn do better stuff with their data?

Mike:

Oh, sure. Okay. So the Economic Graph at LinkedIn is a model of the entities in the economy, focusing on schools that produce talent, people with talent, and companies that absorb that talent, okay, and then companies produce products. So we have things like companies, products, workers, schools, these are main entities, and there are a wide range of relationships between them. So when we want to find a worker who fits in a company in a particular position, we need to have a detailed and reliable description of both the position and the worker.

Mike:

This is what LinkedIn's technology is all about, is matching workers to openings, or now increasingly doing other things, like matching, in their ads, business matching products to people, kind of thing. So knowledge graphs are all about making matching work more systematically and in a more understandable way.

Mike:

So this is what we did at LinkedIn. We built up a kind of vocabulary for describing people or workers and for describing jobs, but we used the same vocabulary for both, so that we could translate, as it were, your worker profile and this company's job profile into a same meta-language. And it made it much easier and much more accurate to compare one with the other. And that meta-language is what we call a knowledge graph.

Larry:

And some of the mechanisms that permit that ... I work mostly in the content world, and we are famous for being bad at our core competency, which is naming and labeling things. And so there's a lot of people doing content jobs that they're doing the same thing, but they have a different job title. I know there's techniques in the knowledge graph world for resolving that kind of discrepancy. Can you talk a little bit about ... and I assume that must have happened at scale in the economic graph.

Mike:

Yes. Oh yeah. Yeah. So when I built a team there, we faced a little problem of having 150 million distinct job titles to navigate. So this is way bigger than anything that normal taxonomists usually deal with. So we had to cut that problem down to size,

François Scharffe and Thomas Deely

The Knowledge Graph Conference is one of the premier events in the semantic technology space.

François Scharffe and Thomas Deeley started the conference to bridge the gap between academic researchers and industry practitioners. The community they have built around the conference and the conference programming - a mix of workshops, classes, presentations, and demos - reflect this purpose.

They also intend to democratize knowledge graph use, and toward that end are participating in a number of efforts to develop education programs - both professional certifications and academic curricula.

We talked about:

the origins of the Knowledge Graph Conference, and its balanced inclusion of both academic and industry entities

how generative AI is propelling interest in knowledge graphs

their mission to broaden awareness of knowledge graph technology and practice

the importance of education and the need for a practical approach

the origins of the Open Knowledge Network, part of the National Science Foundation's Proto-Open Knowledge Network program, and KGC's role in it

their ambition to build an educational institute around KG technology

how to help enterprise executives understand the benefits of knowledge graphs

how modeling an enterprise's knowledge and capturing it in a knowledge graph can help organizations address complex challenges

the advantages of knowledge graphs over LLMs and GenAI, which have yet to prove their reliability

how LLMs can assist in the construction and use of knowledge graphs

how study of the human brain illustrates how GenAI and KGs can work together

the community that has arisen around the Knowledge Graph Conference

François' bio

François Scharffe is a hands-on technology executive with a track record of improving decision making in complex data environments. His career as a technical leader and entrepreneur has led him to perform engineering, product management and leadership roles in various organizations.

François has also worked as a lecturer and researcher, most recently at Columbia University (New York) and at the University of Montpellier (France). François is the founder and chief executive of The Data Chefs, a data management consulting firm, and the founder of the Knowledge Graph Conference, the leading event on knowledge-centric AI technologies.

Thomas' bio

Thomas Deely is co-founder of The Knowledge Graph Conference and Community, a global community bridging research and industry on Knowledge Graphs, AI, and related technologies. Thomas is also the Customer Community Manager at Box, the leading cloud content management platform.

Thomas started his career as an engineer at JPMorgan in London, before joining Goldman Sachs where he advanced to become a senior engineer in the NY office. Thomas launched an Applied Analytics program and executive education initiatives at Columbia University, before venturing into the customer experience, product, and community domain at companies such as Unqork, where he launched the Community, and Stack Overflow, where he helped grow and develop the StackOverflow for Teams product and business, as part of the customer success team, before joining Box.

Thomas has an electronic engineering undergraduate degree from University College, Dublin, and a Masters in Science in Technology Management from Columbia University, and lives in NY where he is married with two children.

Connect with François and Thomas online

François at LinkedIn

Thomas at LinkedIn

Video

Here’s the video version of our conversation:

https://youtu.be/gjtoPXY3Ka8

Podcast intro transcript

This is the Knowledge Graph Insights podcast, episode number 1. I'm really happy to launch this new podcast with a conversation with François Scharffe and Thomas Deeley, the founders of The Knowledge Graph Conference. Their annual gathering in New York City attracts knowledge graph practitioners, researchers, and vendors from around the world for a full week of workshops, presentations, and tech demos. To further advance their democratization of knowledge graphs, they're also launching new educational offerings.

Interview transcript

Larry:

Hi, everyone. Welcome to episode number one of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show François Scharffe and Thomas Deely. They are co-founders of The Knowledge Graph Conference. François is an independent consultant and an entrepreneur. He's also got an academic position he's on leave from right now. Thomas manages... A community manager for Box, a content sharing cloud application. So welcome, folks.

Larry:

François, do you want to tell the folks a little bit more about what you're up to these days?

François:

Sure. Hey, Larry. Thank you for the invitation. Very glad to be here. Such an honor. First episode of what will be another eight years long-running podcast hopefully. So very cool. Thanks.

François:

Yeah, I'm up to a number of projects. I'm kind of emerging off parenting season with two small kids. And I'm looking at different opportunities always around AI and knowledge engineering, neuro-symbolic AI. And in that context, I'm looking at different topics and domains.

François:

I won't go into the details into the projects, but I look at things like agriculture, climate. I look at education very strongly, and I think we are going to talk about this more today. And I also look at personal knowledge graphs. We published a book last year on that topic, and I think that's an interesting space there. There's a lot that we could do with that.

Larry:

Cool. Thanks. And Thomas, what are you up to these days?

Thomas:

Yeah. So also thanks Larry for hosting us for your first in this series. I expect it'll be the first of many. So my day job, I work at Box, where I'm community manager. Box is a SaaS application which makes it easy for people to securely share content internally and externally. And it's interesting times right now with AI, generative AI, and Box has some interesting propositions in that space.

Thomas:

And then my passion project is obviously KGC. And this year we were excited to get some NSF funding to build the community around knowledge graphs around education and community. So that's also something that I'm really excited about. Yeah.

Larry:

Well, let's talk, there's a nice bundle of stuff to talk about there. I want to talk first about the founding of KGC because that's kind of where all this fun stuff starts. But very quickly, after this comes the Open Knowledge Network and the educational stuff. I want to talk about that whole bundle of activities.

Larry:

So how did you... First of all, if I recall correctly, KGC came up when you were both at Columbia. Is that how it happened... Or tell me the start.

Thomas:

Yeah. So we were both working at Columbia University. And I was building out an executive education program and looking for faculty to come up with ideas. And I connected with François and he had the idea of taking, he felt there had been a lot of academic conferences on this domain, and doing something very industry focused.

Thomas:

So that was the genesis. We got some budget from Columbia to get it off the ground. And the first conference, May 2019, went really well, and that was really the genesis of KGC.

Larry:

Cool. And François, can you tell me more about that connection. Because this is something that's always struck me about the conference, is the connection between the academy and industry. Were you the mastermind behind that part of it?

François:

Well, so mastermind is a big term. But basically for me, it was building my ideal conference, the one I missed and I would love to attend. I had worked for half of my career in academia, and there were many events in the space on that topic in academia. But after that, I devoted to mostly industry. And there were no events that were about this topic and relevant.

François:

And so being at Columbia University in New York, having tons of contacts in the field, it sounded like, well, maybe we should just start it, start that event. And then naturally, evolve from the contacts. Having contacts both in industry and in academia, we could reach and invite speakers, and there's that flare. So over time, we've evolved it more towards the industry side. But indeed, we have a lot of... Our community also has a lot of academics in it.

François:

I think bridging the gap is very important. There are a bunch of things that are important and that were the goals, like the original goals. One is really... Originally the real one was say, "Hey, knowledge graphs are not an academic topic." There's tons of people using it for solving real problems in the industry, knowledge graphs in production. Let's give them a voice. Let's hear them. Let's have a forum where we can share our experience, our issues.

François:

But also let's bring academia to tell us about state of the art, but also to hear about what the problems actually working with this technology in the real life, what their problem are. So that can influence researchers to say, "Maybe we should give priorities to this problem in our research because they're more important than others," to give you an example of the kind of interactions that is making possible. Yeah. I'll stop there.

Larry:

Thomas, did you have anything to add to that? Because I know you're.. I perceived of you as more the business... I mean, you're both academics, of course, but...

Thomas:

Yeah. I think this is a fascinating space. It's arguably one of the most interesting spaces in technology. And particularly generative AI is propelling this field forward. And I think the goal was to bring an industry lens to a really interesting space, democratize it, raise awareness.

Thomas:

So our mission, we had a tagline at the conference this year is,