Intro

Jesús Barrasa Over his 20-year career, Jesús Barrasa has spanned the worlds of object-oriented property graphs and assertion-based knowledge graphs. He knows as much about these two foundational technologies as anyone and offers pragmatic advice to help architects and engineers decide which approach will work best for their needs. We talked about: his role at Neo4j in which he helps companies adopt graph technology his academic study of semantic technology and his early work on mapping relational data to ontologies and enterprise ontologies his move across the graph spectrum from RDF graphs to property graphs, culminating in his current role at Neo4j his take on the similarities and differences between RDF and property graph approaches, the key commonality being linked data and the key distinction between them being the level of abstraction that they employ different ways to approach inference the origins of the semantic web and how it has made data actionable and interoperable and smarter how the professional backgrounds of software developers can affect their choice of graph technologies the crucial role of interoperability in graph technology, and our ongoing inability to productively harness it how semantics is managed and used in the property graph and RDF worlds ontology as a technology-independent way of representing knowledge the importance of staying focused on the needs of practitioners when advising them on how to make a graph technology choice how knowledge graphs can balance the "opaque power" of large language models with "explicit, declarative, explainable power" Jesús' bio Dr. Jesús Barrasa is Neo4j's AI Field CTO and the company's resident expert in Knowledge Graphs and Semantic Technologies. He co-authored the O'Reilly book "Building Knowledge Graphs: A Practitioner's Guide" (released in July 2023) and combines over 20 years of professional experience in the data management space split between industry and research and academia. Prior to joining Neo4j, Jesús worked for data integration companies like Denodo and Ontology Systems (now EXFO), where he gained first-hand experience with many successful enterprise-wide data integration deployments and large graph technology projects enhancing the operations and analytics of major companies worldwide. Jesús' doctoral work in Artificial Intelligence and Knowledge Representation focused on the automatic repurposing of legacy data as knowledge graphs. He's an active thought leader in the graph and semantics communities and co-hosts the popular monthly webcast on knowledge graphs "Going Meta." Connect with Jesús online LinkedIn Going Meta webcast. Video Here’s the video version of our conversation: https://youtu.be/7WFP_oDQsxI Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 18. If you search for the term "knowledge graph," you're likely to get an equal number of results about property graphs and RDF-based graphs. Jesús Barrasa has been immersed in both of those technologies for more than 20 years. He takes a pragmatic approach to graph technology adoption, focusing on the needs of practitioners and on the ability of knowledge graphs to balance the "opaque power" of large language models with the explainable power of knowledge graphs. Interview transcript Larry: Hi everyone. Welcome to episode number 18 of the Knowledge Graph Insights Podcast. I am really delighted to welcome to the show Jesús Barrasa. Jesús is about as graph-ey a person as it gets. I got to say he has a 20-year background in this stuff. He's currently the AI field CTO, Chief Technical Officer for the graph database company Neo4j. Welcome to the show, Jesús. Tell the folks a little bit more about what you're doing these days. Jesús: Hi Larry. Thank you very much. I'm really, really glad to be here. I mean, we've been trying to plan this for a while now. Really, really happy to have this conversation today. So yeah, you're right. I'm with Neo4j. And well, I've always been part of the... We call the field organization so we were not part of building our product, but helping our customers adopting it and adopting graph technology in the general case. These days we have a very, very strong focus on as it's inevitable in how we integrate with large language models, GenAI, which we'll talk a little bit about later on in the show but that's what we do. So basically I work all over the globe with all of our customers, many of our customers of course, and helping them adopting graph technology. So that's where I am today. Larry: That's great. This show, I really like to focus on adoption and use and practice around graph technology and you know as much about it as anyone. I don't always start these episodes with a biography, but your background is so interesting. Because I think virtually every guest I've had to this point comes out of the RDF-based, ontologically driven knowledge graph world. And you come out of that world originally, but you're currently in the property graph space. So tell me a little bit about your pathway from your original study and your career. Jesús: Yeah, absolutely. And that's going to age me I guess. But yeah, you're right. Well, I started many others as a university graduate. I went on and I did computer science and started doing software engineering. But what it really starts for our conversation today is when I decided to join the, it was called the Ontology Engineering Group in Madrid and started my PhD. And that's when I met these people that were using these super interesting technology that was called Semantics, the Semantic Web. Of course it was back in the day when Berners-Lee and company published the famous article in Scientific American. Jesús: And that was amazing. I really, really enjoyed it and finished my PhD and this was the early 2000s, so around 2008 or something like that. And what I did basically was coming up with a way of mapping relational data. I don't want to go too nerdy too early in the conversation, but mapping relational data. Which is basically where all the data lived to ontologies. Basically overlaying some form of semantic description of the meaning of the data. And like many others we try to come up with a declarative, structured mapping language to be able to basically leverage in the semantic web the content from relational databases. So that was great fun. And that's work that then later on people like Juan Sequeda, who we talked about earlier today followed and even built a company around it. But that's what I did. Jesús: And then from there on, I moved to the UK to London to join a company called Ontology that's still around under a different name that we're using the RDF stack. And they were focusing on the telecoms verticals. So we were building connected representations of, I would say all the elements involving the delivery of a telecom service. I mean from the physical infrastructure to the logical elements. And the services, the products, even the customers. And we were trying to solve problems like impact analysis, root cause analysis. So we did that for a few years and that was again, great fun. Jesús: And that's where I jumped to other side of the graph, let's call it, camp. Or let's call it spectrum, because more kind of a gradual thing. And a short stint at a data integration company called Denodo. Then I joined Neo4j. And I've been with Neo for the last nine years, so quite a while. Which means that I've been doing graphs for close to 20 years which is crazy. And like you say, I'm one of these unusual individuals out there that has spent as much time doing RDF and SPARQL as I have been using a property graph and Cypher, which is great. That puts me in a great place for conversations like this one. And it's been great fun. So that's kind of a bit of an overview of what I've been doing over the last over 20 years. Larry: And that's really... Because the way you just said that you're reminding me of, it's just data. And you're getting at it in different ways. You're querying it with SPARQL in some cases in Cypher in another context. But as much about the distinctions or how to navigate that spectrum from property graphs to RDF-based knowledge graphs. How would you describe the similarities? If you do a Google search for knowledge graph, it's a mashup of property graph and RDF-based graph stuff. How would you distinguish the two? And what unites them and what separates them, I guess? Jesús: Sure, yeah, I know, absolutely. And you're totally right. So I don't think we always do a great job at helping practitioner, which is ultimately what should be our main objective. So yeah, I mean the way I see it is they share the most important aspect, which is they have the same underlying abstraction, which is connected data. So we think of the world not in terms of tables or documents. We think of the world in terms of things connected to other things. Because that's how humans think. So we think of Larry is a person, Jesús is a person and we're friends and we're connected through a friendship relationship and we work for companies. And the world is a collection of things related to things. Jesús: Now, if you go down the RDF stack approach you're going to break down that into individual atomic statements that are called triples, which is what RDF is based on. And you would put it in your choice of model would typically, not always it shouldn't be and we can happy to talk about that later, determine kind of a technology stack. But ultimately it's kind of a level of abstraction. If you go the RDF route, you would describe it in terms of statements. So person 123 name Larry, person 123 lives in Amsterdam. Person 123 is connected to person 234. Person 234 is Jesús. So everything can be broken down into logical statement into triples. And that's one way, that's one approach.