Understanding Knowledge Graphs

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.