Enhancing Organizational Processes through Domain Knowledge Models

Andreas Blumauer Every enterprise nowadays is awash in data, content, and knowledge, the understanding of which is all too often available only in employees' heads. Forward-thinking businesses are moving to knowledge graphs to capture that tacit knowledge so that they can better understand and use it. Andreas Blumauer shows how those graphs work best when they're accompanied by a domain knowledge model, creating a "semantic layer" that provides a vivid map of your business knowledge. We talked about: the recent merger of his company, Semantic Web Company, with Ontotext to form a new company, Graphwise, which aims to help enterprises build their semantic layer the 20-year-old origin story behind his definition and description of a semantic layer the emerging trend he sees of data, content, and knowledge people coming together, often around explorations of language and content structure how getting domain knowledge out of people's heads and into multimodal AI architectures can streamline business research how a semantic layer provides a map of your business knowledge the importance of a domain knowledge model in a semantic layer architecture the inadequacy of "desktop data integration," the practice of calling colleagues, consulting varied sources, and otherwise searching through ad hoc enterprise knowledge sources, and the stress it can cause how a domain knowledge model can connect and reveal knowledge across an organization the surprisingly small footprint of the domain knowledge model in an enterprise knowledge graph, typically just 1% or so of the semantic layer how LLMs can help in the discovery of data and the construction of knowledge graphs the two main elements of the semantic layer: domain knowledge models (taxonomies, ontologies, etc.) and an automatically generated enterprise knowledge graph the different perceptions of the value of the semantic layer across data and content professionals, and how the arrival of gen AI has resulted in them talking together more to each other how the semantic layer can facilitate the alignment of vocabularies and the understanding of data across business divisions the benefits of a hybrid centralized-decentralized/global-local "glocalization" semantic layer strategy Andreas' bio Andreas Blumauer is SVP Growth at Graphwise, and CEO and founder of the Semantic Web Company (SWC), provider and developer of the PoolParty Semantic Platform and leading solution provider in the field of semantic AI and RAG. For more than 20 years, he has worked with more than 200 organizations worldwide to deliver AI and semantic search solutions, knowledge platforms, content hubs and related data modeling and integration services. Most recently, Andreas has been involved in the development of AI-powered ESG solutions for companies and investors. A globally recognized thought leader and author in the field of semantic AI and graph technologies, Andreas has helped define and implement knowledge and AI strategies for various industries and domains. He is the author of "The Knowledge Graph Cookbook: Recipes that Work", a practical guide to building and deploying knowledge graphs in organizations. He is passionate about enabling clients to harness the power of semantic AI and graph technologies to achieve their business goals and make a social contribution. Since 2022, Andreas has focused primarily on developing AI-based solutions that help organizations implement ESG and sustainable systems. Connect with Andreas online LinkedIn Graphwise Resources mentioned in this episode Knowledge Graphs, LLMs and Semantic AI LinkedIn group From Data to Trust: Leveraging Knowledge Graphs for Enterprise AI Solutions webinar Enterprise architecture model that includes a semantic layer Video Here’s the video version of our conversation: https://youtu.be/dEQh6m6zoDE Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 14. Data, content, and other business professionals have long known the benefits of sharing company lore in enterprise knowledge systems. Many of those systems now include a knowledge graph. Andreas Blumauer says that the thing that really lets you leverage your knowledge graph is a semantic layer that includes alongside your graph a human-crafted domain knowledge model that captures, and represents in a computer-readable way, the tacit knowledge in your enterprise. Interview transcript Larry: Hi everyone. Welcome to episode number 14 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Andreas Blumauer. Andreas, you may know him as the CEO and the founder of the Semantic Web Company, one of the venerable companies in the Semantic space. But more recently, his company has merged with Ontotext to form Graphwise, where he's now the SVP, the Senior Vice President of Growth and Marketing. So welcome Andreas, tell the folks a little bit more about what's going on these days. Andreas: Yeah, sure. Thanks Larry. Thanks for the invitation to this great podcast series. Yeah. So yeah, exciting days. Currently experiencing the merger between Ontotext and Semantic Web Company. And actually it has come quite naturally. So we have been working together for many years and inside of our Semantic suite, PoolParty Semantic Suite, we always have been using, at least for many years, GraphDB, the core product of Ontotext. So we got closer and closer, and finally the time has come to execute on this bigger vision, which is all about helping organizations create semantic layers. And this is where we currently stand, and it's definitely a very, very exciting time for all of us and looking forward to the next steps. Larry: Yeah, it's easily the biggest news in the industry lately, but yeah, the whole point of it is to support the semantic layer, and you're one of the first people to talk about that. I mean, if you Google it it shows up in a lot of different contexts. But in the context of these enterprise architectures that support knowledge representation tech, you're one of the first people to talk about that. I don't know how familiar people are or whether they need to have your notion of a semantic layer disambiguated from what they've got, but tell me a little bit about what you think of the semantic layer is. Andreas: Yeah, no, this has been a topic I always was, since the beginning of my career, quite a lot of discussions were around knowledge management and how to bring together digital assets to the actual knowledge people have in their heads, so to speak. And there was this, I would say, first wave of knowledge management discussions where it turned out to be quite clear that on the one side, at that time it was around 2005, six or so, rather let's say new digital community where documents contained data information, and the other side of the community said, it's not knowledge. So in a document, you will never find knowledge, it just can be translated or transformed into knowledge as soon as it has been recognized by human beings as such. And so I was always feeling between sitting between those two communities. Andreas: So on the one side was excited since day one. Okay, where does AI bring us? And on the other side, I was totally understanding, okay, we should not substitute human intelligence at all. So I was always in this kind of tried to bridge those two communities. And so I was thinking to myself, looking at the contemporary enterprise data architectures at those days there was a missing layer. There was something, there was the data layer, there were the document repositories, and on top there were the applications trying to represent some kind of business logics and nothing in between. So there was really a big, big hole. So I thought to myself, at that time there was topic maps around even still, and then RDF came up, semantic web standards, more and more semantic web standards got developed. So I was trying to introduce this new layer and they called it semantics layer at that time already, to the community. Andreas: And everybody stared at me as if I was an alien, really. So I felt like, okay, maybe I said something wrong, it doesn't make sense to the others. And I was a little bit puzzled by that, I thought, but this is still, I mean, and that really kept me going into this direction. I always thought to myself, there is something very important. And now I think we have arrived a new era where the data people, the content people and the knowledge people come together and now they discuss how should a semantic layer look like? And I totally understand each of those have different angles and different ideas how it should be set up. But let's take a closer look at it because it really depends on the use cases on top and the strategies of the AI, the overall AI strategy we all now want to develop driven by Gen AI. Obviously it has become even more important than ever before to have a semantic layer in place in our siloed world, in our siloed environments. Larry: Yeah. And that notion of the, as you were saying that you're making me realize that the LLMs are kind of at that, to my mind, there's emerging at the highest level that architecture, they're really a great interface to a lot of this knowledge that we have, but they're not the best keepers or understanders of that knowledge, whereas the RDF stack of knowledge representation stuff is. Can you maybe talk a little bit about the relationship between LLMs and other AI tech and knowledge graphs and how the semantic layer facilitates their interaction? Andreas: Right. I would start maybe like that. First of all, I think language is something which allows us to develop knowledge without our ability to talk and to use language. We're not able to develop anything more complex than let's say what we need for our survival mode typically.