Tony Seale: The Knowledge Graph Guy – Episode 11

Tony Seale With ten years of semantic data experience and an endless stream of insightful posts on LinkedIn, Tony Seale has earned the moniker "The Knowledge Graph Guy." Tony says there's precious little time for enterprises to prepare their data with the interconnectedness and semantic meaning that it needs to be ready for the coming wave of more powerful AI technology. We talked about: his 10-year history of applying academic knowledge graph insights to commercial work, mostly in the finance industry the yin-yang relationship in his "neuro-symbolic loop" concept that connects creative, generative LLMs and the reliable, structured knowledge provided by knowledge graphs the contrast in reasoning capabilities between LLMs and knowledge graphs how neither formal logic nor probabilistic systems are rarely the right answer on their own, hence the yin-yang analogy the crucial role of understanding and consolidating data, the gold mine on which every enterprise is sitting that describes any organization's unique value the power of understanding the "ontological core" of your business and then projecting it, selectively and strategically, to the world the urgent threat posed by snake oil salesmen and other opportunists coming into the graph world and derailing enterprises' chances to properly exploit their unique data advantage the two crucial characteristics of AI-ready data: connectedness and semantic meaning his work chairing the Data Product Ontology (DPROD) working group, an effort to provide a semantic definition of what a data product is Tony's bio For over a decade, Tony has been passionate about linking data. His creative vision for integrating Large Language Models (LLMs) and Knowledge Graphs in large organisations has gained widespread attention, particularly through his popular weekly LinkedIn posts, earning him the reputation of 'The Knowledge Graph Guy.' Tony’s journey into AI and knowledge graphs began as a secret side project, working from a computer under his desk while employed at an investment bank. What started as a personal passion quickly evolved into an area of deep expertise. Over the past decade, Tony has successfully delivered several mission-critical Knowledge Graphs into production for Tier 1 investment banks, helping these institutions better organise and leverage their data. Now, Tony has just founded The Knowledge Graph Guys, a brand-new consultancy dedicated to making knowledge graphs accessible to organisations of all sizes. Through this venture, he aims to empower businesses with the tools and strategies needed to harness this powerful technology. Connect with Tony online LinkedIn The Knowledge Graph Guys Resources mentioned in this podcast Connected Data London conference Knowledge Graph Conference GraphGeeks podcast DPROD working group Video Here’s the video version of our conversation: https://youtu.be/lkNvCzwhTRY Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 11. Whether they realize it or not, every business on the planet is sitting on a gold mine, the precious data that uniquely positions them in their industry and market. With ten years of AI practice and an endless stream of insightful social media posts, Tony Seale has earned the moniker "The Knowledge Graph Guy." Tony argues that enterprises that fail to grasp the urgent need to consolidate and understand their data will not survive the coming wave of more powerful AI. Interview transcript Larry: Hi, everyone. Welcome to episode number 11 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Tony Seale. Tony is the Knowledge Graph Guy. That's how everybody knows him on LinkedIn, and I think he's earned that moniker. He also does a lot of consulting and work for big investment banks and things in the financial world. But welcome, Tony. Tell the folks a little bit more about what you're up to these days. Tony: Hi, Larry. Thanks for having me on here. Yeah, so as I was saying, I've basically been doing knowledge graphs now for the last 10 years, largely within investment bank, investment, large investment banks. So I'm kind of at the rubber meets the road. Well, that's where I have been. So taking the technology that's been largely in the academic space and actually applying that into production settings. And then I've started trying to share that knowledge and I've become obsessed with the idea of what it would be to connect an organization together. So what would be possible if most of the information within a given organization was connected together? And I'm on a mission to push that forward. As you say, I've just started doing a consultancy to try and accelerate that effort. The last really, I guess, two and a bit years now have been focused really on the space between large language models and knowledge graphs. And hopefully we can talk a bit about that. Larry: Yeah, because I attended the Semantics Conference last week and that's pretty much all anybody was talking about. I mean, there was other conversations of course, but you have a take, and I think one of the things that is becoming clear in my mind is this sort of evolution from just math and LLMs to graph to RAG to graph RAG. And then you have this concept of the neuro-symbolic loop which, is that the evolution of the integration of these technologies? Tell us some more about that. Tony: Yeah, so I guess maybe to frame it at a conceptual level, you can think of the large language models existing in this continuous space. So they're slightly fuzzy, they're probabilistic, they're generative. So they're always guessing at what the next right thing to do should be. They're not structured. And that has within it a huge amount of power because they can explore different pathways. They can be in at least some limited sense of the word creative and imaginative. They can write poetry and do everything that we are familiar with them doing. But you could sort of contrast that type of intelligence with I guess what you would call the existing and what people will rather derogatorily call good old-fashioned AI. The existing approach, which is sort of formal deductive logic and formal reasoning. And really that's what knowledge graphs represent from a data side, like the most flexible structure for doing reasoning over your data. Tony: So what then becomes interesting, it's like, well, can large language models actually do that formal reasoning? And obviously the big AI houses are trying really hard in order to make that happen. They're chucking a lot of money into making that happen. But I think to a certain extent, maybe one day they will kind of get so close that we won't know the difference between it. But to a certain extent it's just a kind of different paradigm, if you like. There an uncertainty there within a generative model, which is just very different from what deductive reasoning is going to be. Tony: So the idea of the neuro-symbolic loop is to try to bring these two systems like a yin and yang, bring the two systems in together closely. So that as close as possible and as lower grain possible level, you are looping between this system one and system two. So system one being the large language model, being a bit creative, being generative, very, very quick. And then the system two being the formal representation of the system in which you working through steps, being able to do structured querying, always getting back reliable results. Larry: I love the visual of the yin and yang symbol as the representation of that tightening loop, because that's a perfect way to look at that. But back to, I want to revisit the notion of reasoning a little bit because you mentioned that and that seems really important. And a lot of the AI fans on LinkedIn these days and the big companies themselves of course have been talking a lot about the reasoning that they can do. Can you contrast that? I heard it referred to at Semantics as pseudo-reasoning, versus the real reasoning that you just mentioned, that deductive logic based systems can bring. Can you talk a little bit more about that, the relative reasoning capabilities and whether... Are they just faking it till they make it or what's going on there? Tony: Yeah, so I mean you're always working in analogies with this stuff because at the end of the day, nobody, not even the people who are very close to this truly understand what's going on inside of a large language model. It's a bit of a mysterious thing what's happening in there. But here's one way of conceptualizing it, that effectively it's kind of doing not a database lookup, but almost like that. This very sophisticated lookup of its training data. So it's seen a huge number of samples of training data. It's able to a certain extent, it's kind of mapped all of that kind of training information as you're doing the different layers within the large language model. It's compressed some of the concepts in there, not in a way that we would necessarily be able to understand in this kind of vector representation of it. But so it's able to use that to go and retrieve these kind of patterns, and to a certain extent sort of merge them together. Tony: So what recently has been done with the kind of Q-star stroke, Strawberry stroke 01, and really all of the others are doing the same thing as well, is that you will take some domain where you have right and wrong answers, for instance coding or mathematics. And then what you will do is you'll get the large language model to simulate loads of different answers to that and then have its kind of reasoning steps of how it got to that particular answer. And then you'll go out to an external verifier, which in the coding thing it's like, "Well, okay, run this unit test. Does the unit test pass or with the maths? Okay well,