Knowledge Graph Insights

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, but my original foundational experience is in information and library science and I worked as an academic librarian in the past with books, museum galleries, library, art, and that really gave me the foundational experience and information and knowledge necessary to do my job. Larry: Nice. We were talking before we went on the air about how, especially your art and gallery and that kind of curation, tell me a little bit about how that ... It seems like that was really foundational to your current practice or it's been influential. Tell me why. Is it there's something about the nature of those collections or what's going on there? Jessica: One of the most critical things when working in that space in galleries, libraries, art. It's called GLAM, which is actually a great acronym for galleries, library, art and museum, and within the GLAM space, provenance and lineage is something that cannot be ignored because you're dealing with works, whether it be print books, art obviously, an attribution to the artist or writer and that's instrumental to building information in library spaces within that domain space. And so that taught me a lot about linking records. There's a super interesting framework that from the library and information science background, it's a framework called FRBR, which is Functional Requirements for Bibliographic Records. And the whole idea is to maintain the manifestation and expression of works, which is the lineage of a piece of art or a book and being able to support proper provenance and attribution of works while maintaining lineage for the benefit of people and machines. Larry: Nice. And it's so clear when you think about how LLMs work and the current state of AI, it's clear for the need for that kind of thing. Well, and that's kind of jumping ahead a little bit too to what you do with this stuff after you've got it all organized. I think in almost every case, whether you're like a data scientist, an information architect, content strategist, a museum curator, whatever, you start with this pile of concepts and words and then people like you turn them in to something useful to both humans and computers. How does that look? Walk us through the top level overview of that. Jessica: So normally no matter what, when I come into an organization or any sort of information environment or data-rich environment, there's usually problems, identified problems. And those usually are not the only problems within an ecosystem, an information or data ecosystem. And so the idea of nomenclature or vocabularies is instrumental. It's the foundation of how we discover and find things within that ecosystem. And so having to look at the current vocabulary and the current environment of how words are implemented, that starting point in understanding culturally and otherwise, how words are used and vocabularies used to not only define a domain space but to help support information discovery and retrieval within a space. Larry: Yeah, because that's the classic ... I mean I think the way most people would think of, they don't know anything else about information architecture, it's about discovery and discoverability and findability. But there's sort of levels to that. I work more in the UX world and there you're like, here's how we're going to talk about things. With content designers and UX writers, that's a common thing. So you just end up with a controlled vocabulary or something like that where you talk about it, but there's sort of a progression from that level all the way out to a full-blown ontology. How does that escalation happen I guess from when does it become clear that you need, "Well we really need to define these things," so I guess that's a glossary right now? Jessica: Well, in looking at that environment, so you have a series or collections and I like to use the word collections of vocabularies. Often they occur or exist as flat lists and there are usually internal belief systems that determine how these vocabularies are structured and implemented. Some are closely guarded. I will give the forewarning that within these spaces sometimes branding and marketing will be also pretty protective over the vocabularies used, but not understanding that these are meant to work on the back end of systems to help facilitate information retrieval and discovery. And so when building for back end systems, the lowest common denominator aside from a flat list or controlled vocabulary is to use something like an ontology like SKOS, which is Simple Knowledge Ontology System. It's a very simple ontology that helps to structure a hierarchy and simple relationships that are machine-readable. And it's something that that one standard and lightweight or upper ontology, there's the characteristics that are included with that, but it's super machine-readable and translatable, but it's also human-readable, which is what's really critical about that structure. Jessica: And so you can not only define parent-child relationships, alt labels or aliases and encode those with ontological labels, but there's rule bases, there's standards included with that which lines up with several other standards that exist for information retrieval on the World Wide Web. So having to structure and include those rule bases that are standards-based as well helps to enforce a certain type of discipline and structure within that information ecosystem and data ecosystem, which helps to guide people towards best practices and not only why hierarchies or taxonomies are important. But the introduction of concepts like thesauri or thesaurus, you can actually build a thesaurus using SKOS. So whether people realize it or not, that actually helps to shape a thesaurus. And then you can also have a very sort of primitive knowledge graph but still a knowledge graph using SKOS. So it's like a nice little primer and entry to the world of structuring data and information to be disambiguated.

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.

Katariina Kari, an expert in building knowledge graph teams for major e-commerce giants like IKEA and Zalando, shares her insights on the practical applications of knowledge graphs. She discusses how these graphs enhance customer experience through improved recommendations and search functionality, often yielding seven-figure sales boosts. The conversation also delves into the essential skills needed for knowledge graph projects and how AI tools like LLMs can transform diverse data into valuable semantic resources, reshaping business operations and coding paradigms.

Vera Brozzoni is a metadata manager at the BBC, blending her academic background in music, philosophy, and humanities with technical practices. She discusses the complexities of managing classical music data, emphasizing the chaotic nature of music history. Vera explains how taxonomy aids in preserving cultural heritage and the importance of allowing music to be understood without current biases. She also envisions how AI can enhance metadata approaches and stresses the need for more artists in the field, bridging the gap between creativity and technology.

Teodora Petkova, a scholar and content marketer specializing in semantic technologies, discusses her work at Ontotext creating a knowledge graph of marketing content. She explores the importance of dialogic communication and metadata for effective interaction in the digital landscape. Teodora highlights how controlled vocabularies enhance understanding among stakeholders and offers insights into the semantic web's transformative potential. The conversation also touches on the synergy between corporate knowledge management and marketing strategies.

Dean Allemang, author of "Semantic Web for the Working Ontologist," dives into the transformative power of the semantic web. He discusses how knowledge graphs can significantly enhance the accuracy of LLM-based question-answering systems. Allemang explores the importance of sharing meaning across the web, the unique structure of RDF, and its role in effective knowledge management. He also tackles the balance of trust in AI and the challenges of ontology design amidst rapid technological advancements.