Navigating the Spectrum of Semantic Structures in Knowledge Graphs

Rebecca Schneider Skills that Rebecca Schneider learned in library science school - taxonomy, ontology, and semantic modeling - have only become more valuable with the arrival of AI technologies like LLMs and the growing interest in knowledge graphs. Two things have stayed constant across her library and enterprise content strategy work: organizational rigor and the need to always focus on people and their needs. We talked about: her work as Co-Founder and Executive Director at AvenueCX, an enterprise content strategy consultancy her background as a "recovering librarian" and her focus on taxonomies, metadata, and structured content the importance of structured content in LLMs and other AI applications how she balances the capabilities of AI architectures and the needs of the humans that contribute to them the need to disambiguate the terms that describe the span of the semantic spectrum the crucial role of organization in her work and how you don't to have formally studied library science to do it the role of a service mentality in knowledge graph work how she measures the efficiency and other benefits of well-organized information how domain modeling and content modeling work together in her work her tech-agnostic approach to consulting the role of metadata strategy into her work how new AI tools permit easier content tagging and better governance the importance of "knowing your collection," not becoming a true subject matter expert but at least getting familiar with the content you are working with the need to clean up your content and data to build successful AI applications Rebecca's bio Rebecca is co-founder of AvenueCX, an enterprise content strategy consultancy. Her areas of expertise include content strategy, taxonomy development, and structured content. She has guided content strategy in a variety of industries: automotive, semiconductors, telecommunications, retail, and financial services. Connect with Rebecca online LinkedIn email: rschneider at avenuecx dot com Video Here’s the video version of our conversation: https://youtu.be/ex8Z7aXmR0o Podcast intro transcript This is the Knowledge Graph Insights podcast, episode number 25. If you've ever visited the reference desk at your local library, you've seen the service mentality that librarians bring to their work. Rebecca Schneider brings that same sensibility to her content and knowledge graph consulting. Like all digital practitioners, her projects now include a lot more AI, but her work remains grounded in the fundamentals she learned studying library science: organizational rigor and a focus on people and their needs. Interview transcript Larry: Hi, everyone. Welcome to episode number 25 of the Knowledge Graph Insights podcast. I am really excited today to welcome to the show Rebecca Schneider. Rebecca is the co-founder and the executive director at AvenueCX, a consultancy in the Boston area. Welcome, Rebecca. Tell the folks a little bit more about what you're up to these days. Rebecca: Hi, Larry. Thanks for having me on your show. Hello, everyone. My name is Rebecca Schneider. I am a recovering librarian. I was a trained librarian, worked in a library with actual books, but for most of my career, I have been focusing on enterprise content strategy. Furthermore, I typically focus on taxonomies, metadata, structured content, and all of that wonderful world that we live in. Larry: Yeah, and we both come out of that content background and have sort of converged on the knowledge graph background together kind of over the same time period. And it's really interesting, like those skills that you mentioned, the library science skills of taxonomy, metadata, structured, and then the application of that in structured content in the content world, how, as you've got in more and more into knowledge graph stuff, how has that background, I guess... what's been the transition like as you start to consider knowledge graphs in your content work? How's that been going? Rebecca: Well, I mean, librarians, we're all about organizing things, and we like to organize stuff, and this is just sort of the next step in helping organize stuff so people can find things. I mean, that's what we're all about, right? We want to help people find things. So moving into more and more sophisticated mechanisms that help people not only find things, but leverage content in new and different ways and useful ways is, I think, just a logical progression. Larry: Yeah, that's really... that content discovery, like finding things, discovery, and uncovering things, that's sort of always been the information architecture-y part of content practice. But how is that changing? We were talking a little bit before we went on the air about the emergence of LLMs and then the ensuing interest in knowledge graphs associated with that. Are you finding different challenges in helping machines discover stuff as opposed to humans? Rebecca: Well, okay, there's a couple of different aspects to that. One is that, as we know, structured content is so very important for the success of LLMs, AI applications, et cetera. And the thing is, I have to convince my clients to take the time to clean their house, so to speak, and say, "Okay, you need to clean up your data. You need to clean up your content. You need to get rid of the old, outdated, trivial, extraneous stuff so you have a clean basis to start from." And convincing clients to take that time is a bit of a struggle because they see all the fancy LLMs and all different wonderful things people are doing, and they just want to jump in with both feet, which is great, but you need to have a solid basis first. So it takes some convincing to have people take the time to do that. Rebecca: And then on the LLM side, we have to acknowledge that there are many different kinds of LLMs with different pros and cons depending on what the use case is, and you need to not only understand the LLMs, but also the organization's business drivers, what are their goals, what are their objectives, what are they trying to do with this? Because it's not just to write me a poem in the style of e.e. cummings about apples or something. There are definite business drivers and goals because a lot of money is putting a lot of funds into these kinds of technologies, and you got to make sure that you're getting your bang for your buck. Larry: Yeah. As you say that, you're reminding me that everybody's wrestling, you're not wrestling, but figuring out how to do work with RAG architectures and graph RAG and all these hybrid AI architectures. And I love the way that it sounds like your work... you start with the business goals and kind of back up from there, which seems like a good approach. Are you discovering any patterns or approaches as you figure out how do you balance... because that's an interesting triangle of needs, the business part of the client needs. There's sort of content needs, and then the capabilities of the LLMs and the other tooling and these architectures. That's got to be a lot, it sounds like. Rebecca: It is, and you also have to think about how much work am I going to make people do. You can't train everybody to be prompt engineers, right? And so you need to think about from their perspective, what they're trying to get out of whatever application you're creating, and also their interaction with it to the extent to which, yes, the information is in there, but you have to ask better questions. Does that mean I have to retrain people on how to ask better questions? To what extent is that necessary, or are there other ways of leveraging how we use an LLM in this sort of hybrid structure? How can we use ontologies, et cetera, informing the knowledge graph to help the user so they don't have to be prompt engineers, that they can get what they need with the minimum of fuss. Larry: Yeah, that's really interesting. As you say that, using ontologies to inform the knowledge graph, I was just kind of assumed in my head that ontologies were a prerequisite for knowledge graphs, but I had Jessica Talisman on a while back, and she makes the case that you can judge a lot just with a SKOS-based thesaurus. Do you consider that kind of continuum of semantic sophistication from just term lists to taxonomies to thesauri to full-blown ontologies, are you playing across that spectrum in your work? Rebecca: Yeah, absolutely, absolutely. And everybody... not everybody. A lot of people say, well, taxonomy, and they use it to refer to controlled vocabularies, ontologies, et cetera. To them, it's all taxonomy. It's not, actually. It is a spectrum. It is a progression of, okay, I've got my control vocabulary, then I have hierarchical structure, and then I have synonyms and use-for and all of that kind of stuff. And then you have the ontology. So it's definitely to my mind, a spectrum. And sometimes you don't need the full-blown... I agree. You don't need the full-blown ontology. You can do a lot with a well-structured, well-thought-out taxonomy in an SKOS architecture, and you can really leverage that and you might not need to go the full ontology route. Baby steps. Larry: Yeah. Well, I think baby steps, that's a lesson that comes up everywhere. And that's it. And some of those baby steps, so how... I've talked to a lot of people about this from different backgrounds, and this seems to come really naturally to librarians, or people out of a library science background, that comfort with that spectrum of options for how to organize things is that's kind of a... I didn't go to library science school. I've hung out with you all a lot. But is that sort of just a part of the mindset of a librarian? Rebecca: Yeah, I mean, a lot of people actually become librarians as a second career. When I went to school back in the day,