Jans Aasman Hybrid AI architectures get more complex every day. For Jans Aasman, large language models and generative AI are just the newest additions to his toolkit. Jans has been building advanced hybrid AI systems for more than 15 years, using knowledge graphs, symbolic logic, and machine learning - and now LLMs and gen AI - to build advanced AI systems for Fortune 500 companies. We talked about: his knowledge graph and neuro-symbolic work as the CEO of Franz the crucial role of a visionary knowledge graph champion in KG adoption in enterprises the two types of KG champions he has encountered: the magic-seeking, forward-looking technologist and the more pragmatic IT leader trying to better organize their operation the AI architectural patterns and themes he has seen emerge over the past 25 years: logic, reasoning, event-based KGs, machine learning, and of course gen AI and LLMs how gen AI lets him do things he couldn't have imagined five years ago the enduring importance of enterprise taxonomies, especially in RAG architectures which business entities need to be understood to answer complex business questions his approach to neuro-symbolic AI, seeing it as a "fluid interplay between a knowledge graph, symbolic logic, machine learning, and generative AI" the power of "magic predicates" a common combination of AI technologies and human interactions that can improve medical diagnosis and care decisions his strong belief in keeping humans in the loop in AI systems his observation that technology and business leaders seeing the need for "a symbolic approach next to generative AI" his take on the development of reasoning capabilities of LLMs how the code-generation capabilities of LLMs are more beneficial to senior programmers and may even impede the work of less experiences coders Jans' bio Jans Aasman is a Ph.D. psychologist and expert in Cognitive Science - as well as CEO of Franz Inc., an early innovator in Artificial Intelligence and provider of Knowledge Graph Solutions based on AllegroGraph. As both a scientist and CEO, Dr. Aasman continues to break ground in the areas of Artificial Intelligence and Knowledge Graphs as he works hand-in-hand with numerous Fortune 500 organizations as well as government entities worldwide. Connect with Jans online LinkedIn email: ja at franz dot com Video Here’s the video version of our conversation: https://www.youtube.com/watch?v=SZBZxC8S1Uk Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 20. The mix of technologies in hybrid artificial intelligence systems just keeps getting more interesting. This might seem like a new phenomenon, but long before our LinkedIn feeds were clogged with posts about retrieval augmented generation and neuro-symbolic architectures, Jans Aasman was building AI systems that combined knowledge graphs, symbolic logic, and machine learning. Large language models and generative AI are just the newest technologies in his AI toolkit. Interview transcript Larry: Hi, everyone. Welcome to episode number 20 of the Knowledge Graph Insights podcast. I am really delighted today to welcome to the show Jans Aasmann. Jans is, he originally started out as a psychologist and he got into cognitive science. For the past 20 years, he's run a company called Franz, where he's the CEO doing neuro-symbolic AI, so welcome, Jans. Tell the folks a little bit more about what you're doing these days. Jans: We help companies build knowledge graphs, but with the special angle that we now offer neuro-symbolic AI so that we, in a very fluid way, mix traditional symbolic logic and the traditional machine learning with the new generative AI. We do this in every possible combination that you could think of. Larry: Who? Jans: These applications might be in healthcare or in call centers or in publishing. It's many, many, many different domains it supplies. Larry:

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.

Jesús Barrasa, Neo4j's AI Field CTO and a seasoned expert in Knowledge Graphs, shares insights from his 20 years in data management. He discusses the practical adoption of graph technology, emphasizing the nuanced differences between RDF and property graphs. Jesús highlights the role of semantics and ontologies in enhancing data representation and explains interoperability challenges in enterprise systems. He explores how large language models can synergize with knowledge graphs, encouraging a curious mindset for continued engagement in the field.

Yaakov Belch, an independent data scientist, dives into the crucial topic of human control over AI systems, arguing for a shift from 'humans in the loop' to 'humans in control.' He discusses the application of AI and knowledge graphs to business contracts, emphasizing the need for clarity and intent in contractual dealings. Yaakov also explores the importance of human oversight in interpreting ambiguous situations, the responsibility of users in understanding AI outputs, and how structured knowledge graphs can bridge gaps in decision-making. His insights reveal practical applications that can unlock new business value.

Michael Iantosca, Senior Director of Knowledge Platforms and Engineering at Avalara, brings over 44 years of expertise in content management and AI. He discusses the transition from static to dynamic content management using deterministic models like knowledge graphs. Iantosca highlights the importance of ontology skills in teams and the combined strength of deterministic and probabilistic approaches for content retrieval. He emphasizes that content is an evolving asset and advocates for effective integration between knowledge management and AI for superior content experiences.

In this discussion, Fran Alexander, an independent taxonomist and ontologist, draws fascinating parallels between the Alien vs. Predator franchise and the realms of LLMs and knowledge graphs. She explores how knowledge graphs offer structured, predictable frameworks, while LLMs are unpredictable and complex. Fran highlights the issues of bias and transparency in LLMs and the importance of combining their strengths with knowledge graphs for enhanced AI outcomes. Ultimately, she emphasizes how taxonomists can harness LLMs in decision-making and taxonomy building.

Andreas Blumauer, SVP Growth at Graphwise and founder of the Semantic Web Company, dives into the transformative power of knowledge graphs for enterprises. He discusses how merging tacit knowledge with a semantic layer can revolutionize data management. Topics include the importance of integrating domain knowledge into AI architectures, the stress of inadequate data integration methods, and how a semantic layer maps organizational knowledge. Blumauer also highlights the synergy between large language models and knowledge graphs, enhancing collaboration across teams.

Jessica Talisman Jessica Talisman is a seasoned information architect with decades of experience across a variety of domains. She's done a lot of education and outreach around her semantic and and information architecture practices. One of the most important lessons she's learned is the crucial role of standards like the W3C SKOS model to bring structure and semantics to information and knowledge systems. Since there are never enough information architects in any organization, she supports the democratization of IA practices, but she's also quick to highlight the unique skills that you can only get with deep study. We talked about: her work as a senior information architect at Adobe and previously in GLAM (galleries, libraries, art, and museums) and other domains how her work in GLAM showed her the importance of the concept of lineage and attribution and benefits of the FRBR (Functional Requirements for Bibliographic Records) framework how standards and rules bring discipline and structure to information and data ecosystems how capturing knowledge via the SKOS standard can provide on its own the structure, semantics, and disambiguation your data needs, as well as set you up for future successes the importance of focusing on semantic fundamentals and how the ensuing understanding if your data assets can improve activities like a graph RAG implementation the importance of collaborating and sharing ideas across domains democratization, evangelism, and other kinds of information architecture outreach the "Golden Spike" railroad metaphor she uses to illustrate cross-functional collaboration challenges how linked data can help span organizational silos and align stakeholders on language and terminology the importance of understanding your unique organizational fingerprint how applying the library science concept of "scholarly communications" can move organizations forward and promote innovation Jessica's bio Jessica Talisman is a Senior Information Architect at Adobe. She has been building information systems to support human and machine information retrieval for more than 25 years. Jessica has worked in a variety of domains such as e-commerce, government, AdTech, EdTech and GLAM. Jessica holds a Masters in Library and Information Science with a concentration in Informatics. She lives in Santa Cruz, California with her partner Dave, and two dogs. Connect with Jessica online LinkedIn - Jessica is working on a new book about information architecture and is looking for anecdotes and other input. If you're an IA practitioner with good stories to share, she'd love to connect. Video Here’s the video version of our conversation: https://youtu.be/1tlrZTJ52Vs Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 12. Anyone who has tried to discern how people in a domain talk about the concepts in it, and then try to align stakeholders in an organization around those concepts and the words that describes them, and then share that information with computers so that you can scale the impact of your work, knows that you need a good system to manage your taxonomies and other terminology. Jessica Talisman argues that the W3C SKOS standard is your best friend in such endeavors. Interview transcript Larry: Okay. Hi everyone. Welcome to episode number 12 of the Knowledge Graph Insights Podcast. I am really delighted today to welcome to the show Jessica Talisman. Jessica's currently a senior information architect at Adobe, but she is extremely experienced in information architecture and knowledge graph stuff, so welcome Jessica, tell the folks a little bit more about what you're up to these days. Jessica: Thanks, Larry. So I'm currently, as Larry said, a senior information architect at Adobe, and before this, I was information architect over at Amazon. I've worked in many different domain spaces,

Tony Seale, founder of The Knowledge Graph Guys, shares a wealth of insights from his decade-long experience in semantic data. He discusses the vital necessity for enterprises to ready their data for powerful emerging AI technologies. The conversation highlights the harmonious relationship between generative LLMs and structured knowledge graphs in the 'neuro-symbolic loop.' Seale warns of opportunists in the field and stresses the importance of understanding an organization’s unique data. Ultimately, he emphasizes that AI-ready data must be both connected and semantically rich.

Paco Nathan, a seasoned computer scientist and leader in knowledge graphs at Senzing.com, dives into the emerging world of Graph RAG in AI. He unpacks the vital role of entity resolution in fraud detection and how many valuable knowledge graph projects remain under the radar. Paco introduces his "graph thinking" approach, illustrated with a medieval village model, and emphasizes the need to embrace complexity via the Cynefin framework. Discover how knowledge graphs enhance our understanding of complex environments across diverse industries.