Ole Olesen-Bagneux, a globally recognized authority in metadata management and Chief Evangelist at Actian, discusses the concept of the Meta Grid—a framework that simplifies enterprise metadata management. He explains that metadata exists everywhere in an organization and outlines how the Meta Grid can connect this scattered data. Ole compares the Meta Grid to complex architectures like microservices and Data Mesh, emphasizing its practicality. He also shares insights about his forthcoming book, advocating for a collaborative approach to effective data management.

Andrea Volpini Your organization's brand is what people say about you after you've left the room. It's the memories you create that determine how people think about you later. Andrea Volpini says that the same dynamic applies in marketing to AI systems. Modern brand managers, he argues, need to understand how both human and machine memory work and then use that knowledge to create digital memories that align with how AI systems understand the world. We talked about: his work as CEO at WordLift, a company that builds knowledge graphs to help companies automate SEO and other marketing activities a recent experiment he did during a talk at an AI conference that illustrates the ability of applications like Grok and ChatGPT to build and share information in real time the role of memory in marketing to current AI architectures his discovery of how the agentic approach he was taking to automating marketing tasks was actually creating valuable context for AI systems the mechanisms of memory in AI systems and an analogy to human short- and long-term memory the similarities he sees in how the human neocortex forms memories and how the knowledge about memory is represented in AI systems his practice of representing entities as both triples and vectors in his knowledge graph how he leverages his understanding of the differences in AI models in his work the different types of memory frameworks to account for in both the consumption and creation of AI systems: semantic, episodic, and procedural his new way of thinking about marketing: as a memory-creation process the shift in focus that he thinks marketers need to make, "creating good memories for AI in order to protect their brand values" Andrea's bio Andrea Volpini is the CEO of WordLift and co-founder of Insideout10. With 25 years of experience in semantic web technologies, SEO, and artificial intelligence, he specializes in marketing strategies. He is a regular speaker at international conferences, including SXSW, TNW Conference, BrightonSEO, The Knowledge Graph Conference, G50, Connected Data and AI Festival. Andrea has contributed to industry publications, including the Web Almanac by HTTP Archive. In 2013, he co-founded RedLink GmbH, a commercial spin-off focused on semantic content enrichment, natural language processing, and information extraction. Connect with Andrea online LinkedIn X Bluesky WordLift Video Here’s the video version of our conversation: https://youtu.be/do-Y7w47CZc Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 27. Some experts describe the marketing concept of branding as, What people say about you after you’ve left the room. It's the memories they form of your company that define your brand. Andrea Volpini sees this same dynamic unfolding as companies turn their attention to AI. To build a memorable brand online, modern marketers need to understand how both human and machine memory work and then focus on creating memories that align with how AI systems understand the world. Interview transcript Larry: Hi, everyone. Welcome to episode number 27 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Andrea Volpini. Andrea is the CEO and the founder at WordLift, a company based in Rome. Tell the folks a little bit more about WordLift and what you're up to these days, Andrea. Andrea: Yep. So we build knowledge graphs and to help brands automate their SEO and marketing efforts using large language model and AI in general. Larry: Nice. Yeah, and you're pretty good at this. You've been doing this a while and you had a recent success story, I think that shows, that really highlights some of your current interests in your current work. Tell me about your talk in Milan and the little demonstration you did with that. Andrea: Yeah, yeah, so it was last week at AI Festival,

Jacobus Geluk, CEO of agnos.ai and a leading figure in GraphAI, shares his expertise rooted in over 20 years in data management. He discusses the urgent need for standards in the data-product marketplace, emphasizing the DPROD specification he developed. Geluk illustrates how 'use-case trees' bridge the gap between technical capabilities and business needs, enhancing LLM-driven architectures. He highlights issues of compliance and governance for AI agents while advocating for a strong connection between knowledge graphs and emerging AI technologies.

Rebecca Schneider, Co-founder of AvenueCX, dives into the intersection of library science and modern content strategy. She uncovers how traditional skills like taxonomy and metadata are invaluable in today’s AI landscape. Schneider stresses the importance of user-centered approaches in organizing information while discussing how structured content enhances both human and AI discovery. She highlights the synergy between domain modeling and content strategy, advocating for a tech-agnostic approach in navigating the complexities of knowledge graphs and efficient content management.

Ashleigh Faith, PhD in Advanced Semantics with over 15 years in knowledge graphs, shares her insights on the transformative power of semantic modeling in AI. She discusses her popular YouTube channel, IsA DataThing, and how accurate fact modeling is crucial for large organizations. Ashleigh highlights the importance of focusing on business cases amidst the rise of LLMs, the emergence of intuitive interfaces, and considerations for choosing between property and knowledge graphs. She also expresses hope for more accessible RDF graph experimentation.

Panos Alexopoulos Any knowledge graph or other semantic artifact must be modeled before it's built. Panos Alexopoulos has been building semantic models since 2006. In 2020, O'Reilly published his book on the subject, "Semantic Modeling for Data." The book covers the craft of semantic data modeling, the pitfalls practitioners are likely to encounter, and the dilemmas they'll need to overcome. We talked about: his work as Head of Ontology at Textkernel and his 18-year history working with symbolic AI and semantic modeling his definition and description of the practice of semantic modeling and its three main characteristics: accuracy, explicitness, and agreement the variety of artifacts that can result from semantic modeling: database schemas, taxonomies, hierarchies, glossaries, thesauri, ontologies, etc. the difference between identifying entities with human understandable descriptions in symbolic AI and numerical encodings in sub-symbolic AI the role of semantic modeling in RAG and other hybrid AI architectures a brief overview of data modeling as a practice how LLMs fit into semantic modeling: as sources of information to populate a knowledge graph, as coding assistants, and in entity and relation extraction other techniques besides NLP and LLMs that he uses in his modeling practice: syntactic patterns, heuristics, regular expressions, etc. the role of semantic modeling and symbolic AI in emerging hybrid AI architectures the importance of defining the notion of "autonomy" as AI agents emerge Panos' bio Panos Alexopoulos has been working since 2006 at the intersection of data, semantics and software, contributing in building intelligent systems that deliver value to business and society. Born and raised in Athens, Greece, Panos currently works as a principal educator at OWLTECH, developing and delivering training workshops that provide actionable knowledge and insights for data and AI practitioners. He also works as Head of Ontology at Textkernel BV, in Amsterdam, Netherlands, leading a team of data professionals in developing and delivering a large cross-lingual Knowledge Graph in the HR and Recruitment domain. Panos has published several papers at international conferences, journals and books, and he is a regular speaker in both academic and industry venues. He is also the author of the O’Reilly book “Semantic Modeling for Data – Avoiding Pitfalls and Dilemmas”, a practical and pragmatic field guide for data practitioners that want to learn how semantic data modeling is applied in the real world. Connect with Panos online LinkedIn Video Here’s the video version of our conversation: https://youtu.be/ENothdlfYGA Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 23. In order to build a knowledge graph or any other semantic artifact, you first need to model the concepts you're working with, and that model needs to be accurate, to explicitly represent all of the ideas you're working with, and to capture human agreements about them. Panos Alexopoulos literally wrote the book on semantic modeling for data, covering both the principles of modeling as well as the pragmatic concerns of real-world modelers. Interview transcript Larry: Hi everyone. Welcome to episode number 23 of the Knowledge Graph Insights podcast. I am really excited today to welcome to the show Panos Alexopoulos. Panos is the head of ontology at Textkernel, a company in Amsterdam that works on knowledge graphs for the HR and recruitment world. Welcome, Panos. Tell the folks a little bit more about what you're doing these days. Panos: Hi Larry. Thank you very much for inviting me to your podcast. I'm really happy to be here. Yeah, so as you said, I'm head of ontology at Textkernel. Actually, I've been working in the field of data semantics, knowledge graph ontologies for almost now 18 years, even before the era of machine learning,

Mike Pool, with extensive experience in ontology and semantic technology at Bloomberg and Amazon, argues for a pause on the term 'semantics' to enhance clarity in discussions. He highlights the challenges of defining semantic technology's scope and the importance of aligning technical architectures with end-user needs. The conversation dives into the nuances of language, how to address misunderstandings, and the role of large language models in capturing meaning. Pool emphasizes the need for a domain-focused, pragmatic approach to reusability and scalability in semantic practices.

In this engaging conversation, Margaret Warren, Founder and CEO of Metadata Authoring Systems and creator of ImageSnippets, shares her journey from childhood photographer to metadata expert. She explains the critical role of image metadata in enhancing discoverability and the implications of privacy in today's digital landscape. Margaret discusses the nuances between different types of metadata and the significance of linked data principles. With insights into the future of metadata and its growing importance, she advocates for protecting metadata integrity across platforms.

Jans Aasman, CEO of Franz Inc., is an expert in AI and knowledge graphs, bringing over 15 years of experience in hybrid AI systems. He discusses the rising complexity of AI architectures and the transformative power of hybrid models using knowledge graphs, machine learning, and generative AI. Jans emphasizes the crucial role of visionary champions for successful KG adoption in enterprises. He highlights the importance of human oversight in AI-driven healthcare and the interplay of symbolic logic and generative AI for enhanced reasoning and query generation.

Juan Sequeda is a Principal Scientist at data.world, specializing in AI and knowledge graphs. He explores how large language models can enable broader knowledge graph adoption, offering new discovery capabilities and enhancing decision-making in businesses. Juan emphasizes that a knowledge-first approach unlocks hidden value, turning one plus one into much more. He also critiques the historical slow progress in knowledge systems and advocates for prioritizing semantics as essential for maximizing data initiatives and business efficiency.