Knowledge Graph Insights

Tara Raafat At Bloomberg, Tara Raafat applies her extensive ontology, knowledge graph, and management expertise to create a solid semantic and technical foundation for the enterprise's mission-critical data, information, and knowledge. One of the keys to the success of her knowledge graph projects is her focus on people. She of course employs the best semantic practices and embraces the latest technology, but her knack for engaging the right stakeholders and building the right kinds of teams is arguably what distinguishes her work. We talked about: her history as a knowledge practitioner and metadata strategist the serendipitous intersection of her knowledge work with the needs of new AI systems her view of a knowledge graph as the DNA of enterprise information, a blueprint for systems that manage the growth and evolution of your enterprise's knowledge the importance of human contributions to LLM-augmented ontology and knowledge graph building the people you need to engage to get a knowledge graph project off the ground: executive sponsors, skeptics, enthusiasts, and change-tolerant pioneers the five stars you need on your team to build a successful knowledge graph: ontologists, business people, subject matter experts, engineers, and a KG product owner the importance of balancing the desire for perfect solutions with the pragmatic and practical concerns that ensure business success a productive approach to integrating AI and other tech into your professional work the importance of viewing your knowledge graph as not just another database, but as the very foundation of your enterprise knowledge Tara's bio Dr. Tara Raafat is Head of Metadata and Knowledge Graph Strategy in Bloomberg’s CTO Office, where she leads the development of Bloomberg’s enterprise Knowledge Graph and semantic metadata strategy, aligning it with AI and data integration initiatives to advance next-generation financial intelligence. With over 15 years of expertise in semantic technologies, she has designed knowledge-driven solutions across multiples domains including but not limited to finance, healthcare, industrial symbiosis, and insurance. Before Bloomberg, Tara was Chief Ontologist at Mphasis and co-founded NextAngles™, an AI/semantic platform for regulatory compliance. Tara holds a PhD in Information System Engineering from the UK. She is a strong advocate for humanitarian tech and women in STEM and a frequent speaker at international conferences, where she delivers keynotes, workshops, and tutorials. Connect with Tara online LinkedIn email: traafat at bloomberg dot net Video Here’s the video version of our conversation: https://youtu.be/yw4yWjeixZw Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 41. As groundbreaking new AI capabilities appear on an almost daily basis, it's tempting to focus on the technology. But advanced AI leaders like Tara Raafat focus as much, if not more, on the human side of the knowledge graph equation. As she guides metadata and knowledge graph strategy at Bloomberg, Tara continues her career-long focus on building the star-shaped teams of humans who design and construct a solid foundation for your enterprise knowledge. Interview transcript Larry: Hi everyone. Welcome to episode number 41 of the Knowledge Graph Insights podcast. I am really excited today to welcome to the show Tara Raafat. She's the head of metadata and knowledge graph strategy at Bloomberg, and a very accomplished ontologist, knowledge graph practitioner. And welcome to the show, Tara. Tell the folks a little bit more about what you're doing these days. Tara: Hi, thank you so much, Larry. I'm super-excited to be here and chatting with you. We always have amazing chats, so I'm looking forward to this one as well. Well, as Larry mentioned, I'm currently working for Bloomberg and I've been in the space of knowledge graphs and ontology and creation for a pretty long time. So I've been in this community, I've seen a lot. And my interest has always been in the application of ontologies and knowledge graphs in industries, and have worked in so many different industries from banking and financial to insurance to medical. So I touched upon a lot of different domains with the application of knowledge graphs. And currently at Bloomberg, I am also leading their metadata strategy and the knowledge graph strategy, so basically semantic metadata. And we're looking over how we are basically connecting all the different data sources and data silos that we have within Bloomberg to make our data ready for all the AI interesting, exciting AI stuff that we're doing. And making sure that we have a great representation of our data. Larry: That's something that comes up all the time in my conversations lately is that people have done this work for years for very good reasons, all those things you just talked about, the importance of this kind of work in finance and insurance and medical fields and things like that. But it turns out that it makes you AI-ready as well. So is that just a happy coincidence or are you doing even more to make your metadata more AI-ready these days? Tara: Yeah. In a sense, you could say happy coincidence, but I think from the very beginning of when you think about ontologies and knowledge graphs, the goal was always to make your data machine-understandable. So whenever people ask me, "You're an ontologist, what does that even mean?" My explanation was always, I take all the information in your head and put it in a way that is machine understandable. So now encoded in that way. So now when we're thinking about the AI era, it's basically we're thinking if AI is operating on our information, on our data, it needs to have the right context and the right knowledge. So it becomes a perfect fit here. So if data is available and ready in your knowledge graph format, it means that it's machine understandable. It has the right context. It has the extra information that an AI system, specifically in the LLM era and generative AI needs in order to make sure that the answering that it's done is more grounded and based in facts, or have a better provenance. And it's more accurate in quality. Larry: Yeah, that's right. You just reminded me, it's not so much serendipity or a happy coincidence. It's like, no, it's just what we do. Because we make things accessible. The whole beauty of this is the- Tara: We knew what's coming, right? The word AI has changed so much. It's the same thing. It just keeps popping up in different contexts, but yeah. Larry: So you're actually a visionary futurist as all of us are in the product. Yeah. In your long experience, one of the things I love most, there's a lot of things I love about your work. I even wrote about it after KGC. I summarized one of your talks, and I think it's on your LinkedIn profile now, you have this great definition of a knowledge graph. And you liken it to a biological concept that I like. So can you talk a little bit about that? Tara: Sure. I see knowledge graph as the DNA of data or DNA of our information. And the reason I started thinking about it that way is when you think about the human DNA, you're literally thinking of the structure and relationship of the organisms and how they operate and how they evolve. So there's a blueprint of their operation and how they would grow and evolve. And for me, that's very similar to when we start creating a knowledge graph representation of our data, because we're again, capturing the structure and relationships between our data. And we're actually encoding the context and the rules that are needed to allow our data to grow and evolve as our business grows and evolves. So there's a very similarity for me there. And it also brings that human touch to this whole concept of knowledge graphs because when I think about knowledge graphs and talking about ontologies, it comes from a philosophical background. And it's a lot more social and human. Tara: And at the end of the day, the foundation of it is how we as humans interpret the world and interpret information. And how then by the use of technology, we encode it, but the interpretation is still very human. So that's why this link for me is actually very interesting. And I think one more thing I would add, which is I do this comparison to also emphasize on the fact that knowledge graphs are not just another database or another data store. So I don't like companies to look at it from that perspective. They really should look at it as the foundation on which their data grows and evolves as their business grows. Larry: Yeah. And that foundational role, it just keeps coming up, again, related to AI a lot, the LLM stuff that I've heard a lot of people talk about the factual foundation for your AI infrastructure and that kind of thing. And again, another one of those things like, yeah, it just happens to be really good at that. And it was purpose built for that from the start. Larry: You mentioned a lot in there, the human element. And that's what I was so enamored of with your talk at KGC and other talks you've done and we've talked about this. And one of the things that, just a quick personal aside, one of the things that drives me nuts about the current AI hype cycle is this idea like, "Oh, we can just get rid of humans. It's great. We'll just have machines instead." I'm like, "Have you not heard..." Every conversation, I've done about 300 different interviews over the years. Every single one of them talks about how it's not technical, it's not procedural or management wisdom. It's always people stuff. It's like change management and working with people. Can you talk about how the people stuff manifests in your work in metadata strategy and knowledge graph construction? I know that's a lot. Tara: Sure.

Alexandre Bertails, a software engineer at Netflix's content engineering team, talks about pioneering the Unified Data Architecture (UDA) to enhance semantic interoperability through RDF. He explores the challenge of creating a singular schema for diverse internal needs while detailing the innovative Upper domain modeling language. Bertails highlights Upper's self-describing and self-governing traits, its compact design, and how Netflix operationalized these concepts to improve data management and accessibility across teams. Tune in for insights on making complex data relatable!

Torrey Podmajersky, a seasoned UX/content strategist and president of Catbird Content, dives into the intricate world of semantics in design. Influenced by her father's philosophical pursuits, she explores how language shapes user experiences. Torrey highlights the importance of prelecting to bridge implicit knowledge gaps and the need for cross-functional teamwork in crafting effective product language. She also discusses the Cyc project and contrasts the decision-making processes of human writers with LLMs, revealing the profound impact of semantic design.

Casey Hart, Lead Ontologist for Ford and ontology consultant, blends a rich background in philosophy with practical ontology applications. He explores the philosophical foundations of ontology, revealing how concepts like metaphysics and epistemology shape knowledge graphs and AI. He shares insights from his time at Cycorp, discusses the complexities of defining AI beyond technology, and warns against inflated expectations surrounding AI in Silicon Valley. Hart emphasizes that ontologies are models that can oversimplify reality, making philosophical engagement crucial.

Chris Mungall Capturing knowledge in the life sciences is a huge undertaking. The scope of the field extends from the atomic level up to planetary-scale ecosystems, and a wide variety of disciplines collaborate on the research. Chris Mungall and his colleagues at the Berkeley Lab tackle this knowledge-management challenge with well-honed collaborative methods and AI-augmented computational tooling that streamlines the organization of these precious scientific discoveries. We talked about: his biosciences and genetics work at the Berkeley Lab how the complexity and the volume of biological data he works with led to his use of knowledge graphs his early background in AI his contributions to the gene ontology the unique role of bio-curators, non-semantic-tech biologists, in the biological ontology community the diverse range of collaborators involved in building knowledge graphs in the life sciences the variety of collaborative working styles that groups of bio-creators and ontologists have created some key lessons learned in his long history of working on large-scale, collaborative ontologies, key among them, meeting people where they are some of the facilitation methods used in his work, tools like GitHub, for example his group's decision early on to commit to version tracking, making change-tracking an entity in their technical infrastructure how he surfaces and manages the tacit assumptions that diverse collaborators bring to ontology projects how he's using AI and agentic technology in his ontology practice how their decision to adopt versioning early on has enabled them to more easily develop benchmarks and evaluations some of the successes he's had using AI in his knowledge graph work, for example, code refactoring, provenance tracking, and repairing broken links Chris's bio Chris Mungall is Department Head of Biosystems Data Science at Lawrence Berkeley National Laboratory. His research interests center around the capture, computational integration, and dissemination of biological research data, and the development of methods for using this data to elucidate biological mechanisms underpinning the health of humans and of the planet. He is particularly interested in developing and applying knowledge-based AI methods, particularly Knowledge Graphs (KGs) as an approach for integrating and reasoning over multiple types of data. Dr. Mungall and his team have led the creation of key biological ontologies for the integration of resources covering gene function, anatomy, phenotypes and the environment. He is a principal investigator on major projects such as the Gene Ontology (GO) Consortium, the Monarch Initiative, the NCATS Biomedical Data Translator, and the National Microbiome Data Collaborative project. Connect with Chris online LinkedIn Berkeley Lab Video Here’s the video version of our conversation: https://youtu.be/HMXKFQgjo5E Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 37. The span of the life sciences extends from the atomic level up to planetary ecosystems. Combine this scale and complexity with the variety of collaborators who manage information about the field, and you end up with a huge knowledge-management challenge. Chris Mungall and his colleagues have developed collaborative methods and computational tooling that enable the construction of ontologies and knowledge graphs that capture this crucial scientific knowledge. Interview transcript Larry: Hi everyone. Welcome to episode number 37 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Chris Mungall. Chris is a computational scientist working in the biosciences at the Lawrence Berkeley National Laboratory. Many people just call it the Berkeley Lab. He's the principal investigator in a group there, has his own lab working on a bunch of interesting stuff, which we're going to talk about today. So welcome, Chris, tell the folks a little bit more about what you're up to these days. Chris: Hi, Larry. It's great to be here. Yeah, so as you said, I'm here at Berkeley Lab. We're located in the Bay Area. We're just above UC Berkeley campus. We have a nice view of the San Francisco Bay looking into San Francisco, and so we're a national lab, so we're part of the Department of Energy National Lab system, and we have multiple different areas here in the lab looking at different aspects of science from physics, energy technologies, material science. I'm in the biosciences area, so we are really interested in how we can advance biological science in areas relevant to national scale challenges really in different areas like energy, the environment, health and bio-manufacturing. Chris: My own particular research is really focused on the role of genes and in particular the role of genes in complex systems. So this could be the genes that we have in our own cells, the genes in human beings, how they all work together to hopefully create a healthy human being. One part of my research also looks at the role of genes in the environment, and in particular the role of genes inside tiny old microbes that you'll find in the ocean water and in the soil. And how these genes all work together, both to help drive these microbial systems, help them work together and how they all work together really to drive ecosystems and biogeochemical cycles. Chris: So I think the overall aim is really just to get a picture of these genes and how they interact in these kind of complex systems and build up models of complex systems from scales right the way from atoms through the way through to organisms and indeed all the way to earth-scale systems. So my work is all computational. I don't have a wet lab. So one thing that we realized early on is just when you are sequencing these genomes and trying to interpret the genes, you're generating a lot of information and you need to be able to organize that somehow. And so that's how we arrived at working on knowledge graphs, basically to assemble all of this information together and to be able to use it in algorithms to help us interpret biological data and help us figure out the role of genes in these organisms. Larry: Yeah, many of the people I've talked to on this podcast, they come out of the semantic technology world and apply it in some place or another. It sounds like you came to this world because of the need to work with all the data you've got. What was your learning curve? Was it just another thing in your computational toolkit? Chris: Yeah, in some ways. In fact, my background is, if you go back far enough, my original background is more on the computational side and my undergrad was in AI, but this is back when AI meant good old-fashioned AI and symbolic reasoning and developing Prolog rules to reason about the world and so on. And at that time, I wasn't so interested in that side of AI. I really wanted to push forward with some of the more nascent neural network type approaches. But in those days, we didn't really have the computational power and I thought, "Well, maybe I really need to, I actually learned something about biological systems before trying to simulate them." So that's how I got involved in genomics. This was around about the time of just before the sequencing of the human genome, and I just got really interested in this area, a position came up here at Lawrence Berkeley National Laboratory, and I just got really involved in analyzing some of these genomes. Chris: And in doing this, I came across this project called the Gene Ontology that was developed by some of my colleagues originally in Cambridge and at Lawrence Berkeley National Laboratory. And the goal here was really as we were sequencing these genomes and we were figuring out there's 20,000 genes in the human genome, we discovered we had no way to really categorize what the functions of these different genes were. And if you think about it, there's multiple different ways that you can describe the function of any kind of machine, whether it's a molecular machine inside one of your cells or your car or your iPhone or whatever. You can describe it in terms of what the intent of that machine is. You can describe it in terms of where that machine is localized and what it does, and how that machine works as part of a larger ensemble of machines to achieve some larger objective. Chris: So my colleagues came up with this thing called the gene ontology, and I looked at that and I said, "Hey, I've got this background in symbolic reasoning and good old-fashioned AI. Maybe I could play a role in helping organize all of this information and figuring out ways to connect it together as part of a larger graph." We didn't call them knowledge graphs at this time, but we're essentially building knowledge graphs at the time and make use of, in those days quite early semantic web technologies. This is even before the development of all the web ontology language, but there was still this notion that we could use, we could use rules in combination with graphs to make inferences about things. And I thought, "Well, this seems like an ideal opportunity to apply some of this technology." Larry: That's interesting. It's funny we didn't plan this, but the episode right before you in the queue was of my friend Emeka Okoye. He's a guy who was building knowledge graphs in the late '90s, early 2000s, mostly the early 2000s before the term had been coined, and I think maybe even before a lot of the RDF and OWL and all that stuff was there. So you mentioned Prolog earlier, and what was your toolkit then, and how has it evolved up to the present? That's a huge question. Yeah. Chris: I didn't mean to get into my whole early days with Prolog. Yeah, I've definitely had some interest in applying a lot of these logic programming technologies. As you're aware,

Emeka Okoye, a seasoned Knowledge Engineer and Semantic Architect with over 20 years in knowledge engineering, dives into the world of the Semantic Web. He shares insights on the transformative Model Context Protocol (MCP) and its impact on AI applications. Emeka discusses his RDF Explorer, a tool that allows developers easy access to semantic data without specialized language skills. The conversation also touches on the evolution of ontology engineering, his history in tech innovation in Nigeria, and the importance of making semantic technologies accessible globally.

In this discussion, Tom Plasterer, Managing Director at XponentL Data and a leading expert in data strategy, delves into the origins and significance of FAIR data principles. He unpacks how the concept evolved from the semantic web to address the need for discoverable data in research and industry. Tom reveals the 15 facets of the FAIR acronym and emphasizes the critical role of knowledge graphs in implementing these standards. His journey from bioinformatics to data management showcases the importance of collaboration and shared terminology in enhancing data practices, especially in pharmaceuticals and life sciences.

Mara Inglezakis Owens, an enterprise architect at Delta Air Lines, blends her humanities background with digital anthropology to shape user-focused architecture. She discusses how mentoring shaped her approach and emphasizes the need for understanding actual stakeholder behaviors over self-reports. Mara also shares insights on justifying financial investments in her work, the significance of documentation in knowledge engineering, and lessons learned about embracing imperfection in digital systems design. Her human-centered focus exemplifies the evolution of enterprise architecture in modern businesses.

Frank van Harmelen, a leading AI professor at Vrije Universiteit in Amsterdam, discusses the integration of human and machine intelligence. He emphasizes the importance of hybrid collaboration, advocating for AI systems that enhance rather than replace human capabilities. Topics include the emergence of neuro-symbolic systems, the evolution of conversational interfaces, and the challenges of managing interdisciplinary research teams. He also highlights innovative applications of AI in healthcare and the need for a shared worldview to foster effective collaboration.

Join Denny Vrandečić, Head of Special Projects at the Wikimedia Foundation and founder of Wikidata, as he discusses the groundbreaking Abstract Wikipedia initiative. He shares insights on how it aims to democratize knowledge sharing by allowing contributions in any language. Denny reflects on his journey from the creation of Wikidata to exploring how Abstract Wikipedia can enhance multilingual knowledge accessibility. He also dives into the potential of community collaboration and the use of language models to create structured knowledge representations.