In today's fast-paced digital world, the role of taxonomy and metadata has never been more critical. And yet, it can be challenging to shape this narrative in any size organization. Stilling delves into the strategic importance of harmonizing taxonomies and creating consistent semantic layers across the organization—and how to explain this to leadership in a way that resonates. Through three compelling case studies, explore how leading organizations have navigated digital transformation journeys, leveraging metadata to drive foundational changes and infrastructure investments. Learn strategies to position taxonomy, often overlooked during pivotal transformation moments, ensuring it plays a central role in your company's major decisions.

Rehabbing an existing taxonomy presents a unique sense of challenges. How do you get started? Do you rip everything out to the studs? What can be salvaged, and what needs to go? Kaari presents two case studies of taxonomy projects within OpenSesame’s elearning marketplace catalog for insights on how to approach redeveloping a well-used (though perhaps not well-loved) taxonomy. Hear how to combine data with user feedback to make effective decisions about terminology and structure, along with the issues related to stakeholder communication and buy-in. Learn how to set yourself up for a smooth transition from taxonomy redevelopment to taxonomy maintenance.

Whether building a new or revising an existing taxonomy, there are many possible sources for your taxonomy’s terms (concepts and labels). Suggestions from users are important, but there are multiple ways to get user input, some more effective than others. Content analysis is a great tool, but the extent to which it is manual or automated can vary. Learn how to utilize search logs, uncontrolled keywords, automatically extracted terms, and leveraging AI and LLM methods. Hedden also explains how to evaluate legacy controlled vocabularies, and metadata terms and  also addresses glossaries.

Not everything can just be searched. “Aha!” moments deliver value. Exploration leads to insights and surfaces contexts. How do you prepare your content for these user experiences? See some interfaces that integrate public archives and cultural content collections. (They’re public: You can explore them after the talk.) The interfaces show how taxonomies and knowledge graphs play a direct role in the UI (not just in the backend). From there, Degler broadens out to other domains, looking at how knowledge graphs provide focused “discovery glue” across repositories and how taxonomies can signpost insights across departments, partner organizations, or countries.

Taxonomy goals can be extremely ambitious, creating the semantic glue between content and contributing to omnichannel and headless content strategies. However, getting there doesn’t happen overnight: Tight budgets, legacy systems, procedures, and people stand in the way. The challenge is to start with a minimal viable taxonomy (MVT) that the organization can use and implement in the short term, without blocking the taxonomy to grow in the future. van Aalten shares her experiences with taxonomy projects that started with an MVT and over time evolved into a more mature taxonomy. Learn how to decide the systems scope, level of granularity, types of semantic relationships and industry standards to follow. 

The Nomenclature for Museum Cataloging Committee of the American Association for State and Local History is a group made up of museum professionals that maintains a controlled vocabulary for cataloging museum collections of manmade objects. One of the challenges with this is the reconciliation of divergent terms across different historical periods and contexts. Greene and Hill explore solutions and methods of resolving these conflicts and demonstrates how auto-categorization can be used for long-form, unstandardized object descriptions to create deeper tags for collections, enriching and streamlining their cataloging processes and more efficiently leveraging the available human capital.

How do you map and clean large amounts of messy metadata with a tiny team? And how do you do it fast? Greenberg presents an anonymized case study on cleansing millions of assets worth of metadata in a single year using a combination of human review and code. Learn how to prioritize, how to communicate, and how to save money and time by assigning the right tasks to a program instead of a person.

Metadata and taxonomies are the backbone of asset management. Whether you have a DAM, CMS, MAM, or PIM system (or a mix of these), understanding the current and desired workflows of metadata capture is key to success. When and where in the process the data is updated and/or extended and ultimately finalized influences and shapes the taxonomies and their application. Strategic design and integration of flexible taxonomies that are fit for purpose are crucial. Levenson describes approaches for taxonomy development by understanding the “who, what, where, when, why, and how” of multiple workflows and systems. 

Change is inevitable, but designing (and following) a governance framework is much easier said than done. Little & Cantrell explore some of the key pillars of a good governance plan, including business drivers for taxonomy changes, guidelines for balancing proactive and reactive workflows, and communication and training plans using case studies of recently implemented plans. Learn the critical role documentation plays in change management and the types of documentation needed for success. Ratanatharathorn and Duncan describe the governance and maintenance strategies for the Grant Classification Taxonomy, which has been in use since September 2021 at the AWMF. Hear their processes and best practices for understanding and documenting use cases, vetting them, and balancing the perspectives of different user groups in order to cultivate a taxonomy that suits the needs of many. 

There are more than 800 ISO standards related to terminology. ISO 25964 Part 1, which focuses on thesauri for information retrieval, is being reviewed and revised by a working group. Since the last update of the standard in 2011, the scope has expanded to include terminology management and use, the semantic web, linked data, and AI (meaning NLP, machine learning, LLM, etc.) and interoperability between thesaurus management systems and consuming applications. Busch and Hlava explain the key areas of change that have been included in the draft revision including alignment with SKOS and other related standards, alternatives to preferred term, representative visual resources, persistent identifiers, “multilinguality,” granularity, and others. An interactive Q&A session follows to answer any questions on the standard draft and its potential impact on taxonomy practitioners.

Celebrate the grand opening of the Enterprise Solutions Showcase. Explore the latest products and services from the top companies in the marketplace while enjoying drinks and light bites. Open to all conference attendees, speakers, and sponsors.

A transit authority has undertaken an initiative to eliminate legacy paper records, to downsize their footprint, and relocate 3,000 office-based employees spread across 10 aging buildings. Busch describes the metadata strategy and taxonomy framework that emerged from working with more than 60 business units from across the authority. Learn how the named entities (people, organizations, locations, events, things, etc.), classifications (incidents, document file plans, system maps, access control, etc.) and the relationships among them can be used as the building blocks for a transit system knowledge graph.

In data governance, an enterprise taxonomy program is essential for navigating data complexities, enabling efficient management, and structuring knowledge assets. Hear about how Pettai and her team are expanding Grant Thornton's enterprise taxonomy program and platform through the integration of controlled vocabularies across four enterprise applications. Learn about their approach to taxonomy governance, adoption strategies, and the tangible ROI from this transformative initiative. Attendees gain insights into effective change management and communication strategies, along with artifacts which aid implementing an enterprise taxonomy program.

Sustainability is on the minds of many organizations. The United Nations created a broad outline on sustainability, but providing actionable data is an important additional step. For publishers, creating collections on the topic from their existing materials to aid researchers to quickly find the connections is paramount. The general challenge for organizations with a well-organized collection and existing thesaurus/taxonomy is how to pull out the material on sustainability and still coordinate with the “master” taxonomy, which is true for all collections requiring marketing and showcasing content for specific audiences and needs. Learn how three organizations have approached the topic of sustainability and presentation of materials best suited to answer the questions from both readers and researchers on the topic.

Product data issues can arise in a number of ways from a messy onboarding experience to a bad search experience to even a bad channel grading affecting a company's relationship with distributors. Schweitzer explores different ways that bad data may surface and how to tackle those issues to increase revenue, conversion rates and decrease data production costs.

Taxonomy design often starts with a content audit, but once a taxonomy is in production, how often do we test and improve it with real-time content feedback? A constructive feedback loop—from taxonomy to content and back again—is often limited by technical constraints. Taxonomists may require data science or engineering solutions to sample content, analyze it, and deploy taxonomy improvements. But there is another way: By extending enterprise taxonomies for autocategorization, enterprises can support human-in-the-loop, machine-driven tagging. When you integrate a transparent text analytics service that can be adapted with rules, you can enable nontechnical taxonomist users to power this process: no coding or scripting skills required. If these annotations are stored in a “content aware knowledge graph,” your taxonomy data can power insight into content trends and nuanced content recommendations.

One of the foundational pillars for managing data and content is consistent, centralized, accessible metadata in the form of taxonomies and ontologies. Even when an organization recognizes the value of taxonomies, how do you begin to develop an enterprise taxonomy strategy across siloed, incongruous, and duplicative data systems? How do you scale from a few terms in a spreadsheet to an enterprise taxonomy program laddering up to business goals and key performance indicators? How do taxonomists balance the need to build sustainable semantic models while serving business needs and keeping up with the pace of enterprise delivery? Learn techniques for developing and maintaining a successful enterprise taxonomy strategy even in rapidly changing, complex (perhaps even chaotic) business environments where nothing stands still.

Data annotation for compliance with global privacy regulation requires complex data models—preferably an ontology, but possibly a taxonomy—to bridge the gap between abstract privacy concepts and granular data realities. Building this bridge requires nuanced data modeling but also learning from and supporting diverse perspectives. Policy experts and engineers conceive and describe data significantly differently and that evolves over time. Vann and Downs describe the facets of data that are relevant for privacy: ownership, origin, intent, consent, storage format, semantic meaning. Through working examples, learn how this data can be modeled and explore the trade-offs between modeling nuance and practicalities including UX/UI and incentives.

AI solutions, such as recommendation engines and chatbots ,are gaining traction in solving a multitude of business and content problems. Baquero Cakici illustrates how various AI tools such as recommendation engines, chatbots, and employee 360 views can help solve learning content management challenges. Hear about practical considerations, design methods, and best practices for designing learning taxonomies and ontologies to catalyze AI transformations, and how to get started. Maddison shows how Adobe Learn's Taxonomy as a Service platform delivers unique value by providing an AI-driven tool that unlocks cross-silo insights for their content creators, content strategists, and content experience teams in something they've coined as "MetaHealth." See real-life examples that show how marrying taxonomic metadata with content engagement data can unlock key capabilities. 

Dive headfirst into the world of gaming taxonomies through "Project K": a story of how Electronic Arts (EA) harnesses the power of taxonomies to organize and label a universe of digital assets used in video games—from combat weapons and high-speed vehicles to the minutiae of vegetation and furniture. Hear how assets from two blockbuster EA video games were reprocessed, meticulously labeled, and made accessible for artist reuse. Randle explains how machine learning models were trained using these taxonomies and then used to turbocharge human tagging during the processing pipeline. Discover how taxonomies are reshaping the future of digital asset management and machine learning in the gaming industry.

Celebrate the grand opening of the Enterprise Solutions Showcase. Explore the latest products and services from the top companies in the marketplace while enjoying drinks and light bites. Open to all conference attendees, speakers, and sponsors.

Whether you are brand new to the world of taxonomy or are looking to solidify your foundational knowledge, this workshop equips you with the key concepts to help you hit the ground running on your own taxonomy work. Starting with an accessible, practical examination of what taxonomies are, learn how they fit into the information and content management landscape and the most common use cases, including dynamic content, search and discovery, and reporting. Explore the three pillars of what makes a good taxonomy good: strategy and style (term selection and form, relationships, synonyms, and other properties), governance (roles and responsibilities, processes, and documentation), and technology (technical standards, taxonomy tools, metrics, and analytics needed to implement, integrate, and monitor a taxonomy across platforms). Hear about the more advanced approaches such as knowledge graphs, ontologies, and AI tools. Clark also gives a special deep dive on practical taxonomy change management, including policies, approval workflows, and various methods of versioning and tracking.

Celebrate the grand opening of the Enterprise Solutions Showcase. Explore the latest products and services from the top companies in the marketplace while enjoying drinks and light bites. Open to all conference attendees, speakers, and sponsors.

We need new frameworks for AI-powered decision making that keep humans in the loop (along with human values, morals, interests, emotions, and sensibilities). Rosenberg discusses an approach toward enabling collective superintelligence that is rooted in hundreds of millions of years of evolution, which is why it so greatly outperforms old-school methods that treat humans as mere datapoints to be aggregated. Humans are not data. Humans are powerful data processors. The most viable pathway to collective superintelligence is to connect people together in real time and allow them to act, react, and interact using AI as the interstitial tissue that empowers us to solve problems together in optimal ways. A lifelong technologist, Rosenberg earned his Ph.D, from Stanford University in the early 1990's, was a professor at California State University in the early 2000's and has been focused on enabling collective superintelligence for the last decade. He shares his insights and ideas for enterprises looking for ways to share knowledge in their organizations.

GenAI retrieval-augmented generation (RAG) uses natural language understanding (NLU) and natural language generation (NLG) capabilities of large language models (LLMs) to securely support conversational search and discovery over enterprise content and data repositories. But GenAI and RAG alone are not enough to ensure the completeness and accuracy of information for many mission-critical enterprise applications. Knowledge graphs (KGs), including enterprise taxonomies and ontologies, can significantly improve the completeness and accuracy of information retrieved and generated by GenAI applications. Taxonomies and ontologies provide GenAI with machine-intelligible context about the domain knowledge and processes of the enterprise. When KGs and GenAI are integrated, taxonomists and ontologists can see and rapidly edit graph structures that explicitly guide RAG decision-making processes. With a simple no-code interface, taxonomists and ontologists are empowered to directly control GenAI dependencies, query refinement, and outcomes, thereby delivering high-quality, high-value business process automation. Using real world applications, our knowledgeable speaker illustrates how using knowledge graphs improves enterprise GenAI.

There is no question that GenAI has reignited interest in KM. Gartner predicts that 100% of GenAI virtual customer assistant and virtual agent assistant projects that lack integration to modern KM systems will fail to meet their CX and operational cost-reduction goals by 2025. As businesses experiment with GenAI, they are realizing that robust KM is foundational to its success. Roy discusses how KM and GenAI can accelerate and ensure mutual success, creating transformational business value at warp speed. He shares stunning success stories from clients. Get insights and ideas for your enterprise.

The world of information is exploding, but finding what you truly need can still feel like searching for a needle in a pile of needles. Probstein explores how search and GenAI are joining forces to revolutionize how we discover information. He delves into the lessons learned from traditional search and how AI is pushing the boundaries. He shares real-world examples and discusses how this powerful synergy is shaping the future of information discovery.

Maddison explains how her team introduced the Taxonomy as a Service Platform (TaaS) at Adobe, envisioning a centralized solution for taxonomy design, implementation, and governance for more intuitive client adoption. TaaS revolutionizes the approach to taxonomy, offering a one-stop-shop experience tailored to diverse taxonomy client needs. Also hear how Adobe uses OKRs and KPIs to win over leadership and get things done. Maddison shares OKR and KPI templates for you to use in your journey to taxonomy stardom in your company.

Taxonomists often work solo within an organization but collaborate with a variety of data scientists, content strategists, project managers, and more who may have a limited understanding of taxonomy. Whether you are operating as a de facto internal consultant or planning work as an external consultant or contractor, Griffin shares some “do’s” and “don’ts” of consulting that can set you up for success. Learn these best practices, such as introducing and advocating for taxonomy-driven solutions, effective project scoping, adapting to changing priorities (especially amidst the drive for generative AI-driven solutions), and the art of compromise.

EA's taxonomy team outlines their innovative collaboration with the data science team. Discover how they've redefined taxonomy development by harnessing the power of advanced LLMs to process player support data and using GenAI to generate candidate terms, forming the backbone of their work. Hear about the “human-in-the-loop” approach, where human taxonomy experts meticulously analyzed, clustered, and refined these terms into structured taxonomies adhering to logical standards and best practices. This case study highlights how human intelligence can enhance AI to set new benchmarks in the industry.

Increasingly, the world of data is waking up to the importance of taxonomies and metadata. Hear an end-to-end case study of an enterprise data catalog, from design through implementation, explaining the critical roles that taxonomy and metadata play in data governance, findability, and reuse.

Effective, unbiased machine learning models require clean, consistent, contextual, and well-considered data. Together, taxonomies and ontologies are the semantic layer representing an organization’s subject matter expertise, shared understanding, knowledge, and viewpoint applied to content and data powering a variety of applications. As such, they are at risk of carrying inbuilt subjectivity and bias—a ghost in the machine—flowing into other data consuming systems and machine learning models. Learn processes for modeling, building, and applying semantic models representing the business while reducing the introduction of biases which can skew downstream applications.

As a healthcare learning, credentialing and scheduling company, Healthstream manages lots of user credential information. The knowledge management team is working to make this a more valuable data set by enhancing their Credentials taxonomy with a robust ontology.  This will allow for more nuanced state specific data as well as allow us to bring together useful information that is currently siloed in multiple products and data stores that aren't all using a common vocabulary. The ultimate goal is to create a high-quality curated dataset of credentials that would be easy to verify and report on. 

As the AI rush began, companies created directives to integrate AI into their products to avoid getting left behind. The result of this “AI for AI’s sake” mindset has been a slew of poor implementations and worse outcomes. However, it is possible to know if, when, and how to  integrate AI intentionally into a project by aligning integration with your methodology. Lee explores the spectrum of available tools, ranging from manual effort to advanced techniques leveraging multiple AI techniques. Spoiler: It’s not just LLMs! Jenkins dives deeper into the key use case around using different approaches to validate and enhance metadata tagging workflows to reduce the burden on content creators and improve quality. Hear caveats, considerations, and risks involved in adding AI automations to tagging workflows. Learn the practical applications of AI in taxonomy and tagging illustrated with real-world examples that can be implemented today, as well as insights into what’s on the horizon for tomorrow.

Explore the pivotal role of information architecture (IA) and taxonomy structures in driving organic growth and maximizing the impact of content marketing efforts. Learn how effective IA and taxonomy can enhance organic growth by optimizing content discoverability, improving user experience, and boosting search engine visibility. There’s also a synergy between ontologies and AI in augmenting content marketing strategies for future success. Rafat provides real-world examples to illustrate successful implementations and give you actionable strategies and insights to leverage IA effectively. DiNicola shows how Stand Together tackled a simplification process during its last redesign and replatforming to identify core audiences and negotiate content categorization, building the taxonomy structure that matters and works for users and authors.

Interested in industry trends? Stymied by a taxonomy design challenge at work? Bring your toughest, crunchiest taxonomy issues and challenges to our panel of seasoned full-time taxonomists, who compete to answer your questions with insight, entertainment, and perhaps even controversy! The best questions (as voted by the audience) will bring home prizes!

Stop by the showcase after a full day of stimulating sessions to mix and mingle with other conference attendees, speakers, and our conference sponsors.