Taxonomies and ontologies have had an uncertain relationship for a while now: Is a taxonomy just a special kind of ontology? Is an ontology what a taxonomy wants to be when it grows up? Are they different kinds of things that work better together? Building on lessons learned in a variety of ontology and taxonomy projects in various industries, Allemang proposes a design pattern for the division of labor between taxonomies and ontologies. Aside from its confirmation through a number of industry applications, this pattern also provides opportunities for AI automation that enhance, rather than replace, the role of human expertise in knowledge frameworks.

This introductory taxonomy tutorial covers key concepts to get you up-to-speed for the rest of the conference or helps prepare you to take on a role in a taxonomy project. Topics include the various uses and benefits of taxonomies, comparisons, and suitable applications of different types of taxonomies/controlled vocabularies (hierarchical, faceted, thesauri, and ontologies); taxonomy standards; the relationship of taxonomies to metadata; sources for taxonomy concepts; best practices for developing terms and their relationships; and tools for creating and managing taxonomies.

Barnes compares and contrasts two projects: one for a museum and archives and the other for a retail/ecommerce establishment where system-specific taxonomies and metadata schemas were developed. Hear how stakeholder engagement affected each project and their final deliverables. Learn about the creation process, current state analysis, feedback sessions, and lessons learned along the way for each of the two taxonomy projects, and best practices for maintaining and evolving taxonomies to ensure they stay relevant.

As enterprises wrangle Big Data, taxonomy teams need to work at increasing scale to meet their organization’s challenges. This requires adopting new, data-driven ways of working, embracing new technologies, and integrating metrics-driven thinking into the work of taxonomy teams. Using case studies, Bowes and Downs explore the ways taxonomy teams can rise to the challenge of managing big data with small teams, including building a business case, rapidly hiring and mobilizing a team, embracing new tools, and collecting metrics across the taxonomy workflow.

Launching a new taxonomy is a large undertaking that requires cross-organizational coordination and communication. Many corporate taxonomy projects have extra challenges around change and communication, as their taxonomies can reflect oft-changing external language for clients as well as internal language for enterprise data reporting and tracking. Vilches and Gray share their experiences in designing and implementing enterprise taxonomies at their respective service organizations. Hear about using change management to transcend corporate gravity and maximize adoption, using cross-functional planning to provide stakeholders with an exhilarating experience, and telling the story of taxonomy across the organization and throughout the taxonomy lifecycle in a way that truly gets people on board.

Business changes create taxonomy changes, but what happens when the business needs to maintain both the old taxonomy and the new one? Lee examines an enterprise client making large, structural changes to its organizational structure taxonomy after a major business pivot. Updating the organizational structure taxonomy is important for business operations and analytics, while the company also needs to map back to the previous structure for historical analytics and budget tracking purposes. Even more challenging, the mapping needs to be human-readable and accessible, while allowing for possible future automation. Learn how to balance business needs and limitations with best-practice principles, and how to devise a process for mapping the updated structure back to the previous one.

Even if you follow all standards and best practices for taxonomy and metadata design, you can’t know for sure if a system is working until after it's in production. Jenkins reviews her approach to auditing and improving a taxonomy 1 year after launching a taxonomy-driven website, looking at term looking at metrics, stakeholder feedback, and governance challenges. Ingham & Barkin discuss the pillars of metadata quality (completeness, accuracy, and consistency), sharing a recent experience operationalizing in the context of enterprise digital content management. They cover metadata, quality metrics, and the role of automation in addressing issues.

ASML, leading supplier to the semiconductor industry, set off on an adventure to design and implement an enterprise taxonomy to improve the productivity and “findability” of its information and expertise. In this deep dive case study, hear about their taxonomy project approach, including customized taxonomy development, system selection, tagging strategy, and integration in priority systems. Canter and Cakici highlight the leading use cases for the taxonomy, as well as the challenges, successes, and lessons learned on the journey that led to successful implementation and adoption. Learn how ASML plans to leverage the implementation to centralize and automate taxonomy management and what steps they are taking to continue integration across the organization.

Celebrate the grand opening of the Enterprise Solutions Showcase. Enjoy drinks and light bites while visiting with conference sponsors.

Fragmented healthcare systems and disparate information sources make medical references difficult to navigate for both internal and external stakeholders. Knowing which specialist to reference and in which regional hospital or healthcare center came down to personal knowledge and local directories. Clinia’s Health Directories helped solve this problem by using taxonomies and ontologies in a knowledge graph for search and navigation services. Its multilingual directories are now accessed by more than 3 million users through third-party apps in North America. Solomon and Jousse speak about their process of creating and building an ontology of 10,000 medical concepts with 30,000 relationships for the knowledge graph.

When starting a new taxonomy role, people sometimes assume that developing a large taxonomy for an ever-growing and changing body of content is the most daunting task they face. In reality, taxonomy building can be pretty straightforward compared to everything else that surrounds it, such as securing necessary resources for implementation amidst competing priorities, continually re-introducing the value of taxonomy as generative AI captures attention, navigating turnover and reorganizations, gaining supporters by compiling use cases and pain points, and winning a spot on the road map. Griffin reviews her strategy in advocating for taxonomy-driven solutions and identifies successful approaches for building buy-in and missed opportunities in the context of customer operations for financial services. 

There is a growing need within skills-based organizations to align employee training with skills and roles. Taxonomies can help provide a foundation to align skills to learning content. Simpson discusses Walmart’s current effort to push learning content to associates by providing them skills for their current and next role and recommend learning paths. Hedden looks at issues involved in coming up with taxonomy of skills, the importance of considering the different users of a skills taxonomy, how skills can be linked to roles in a simple ontology, and the challenges in application frontend design.

With privacy an increasing focus of regulators and consumers worldwide, organizations need to find scalable ways to integrate privacy controls into their technology stack. Taxonomy and ontology can serve as privacy-enabling technology. Using specific case studies, Vann and Downs explore different methods for enabling privacy controls through taxonomy or ontology as well as the pros and cons of each.

Machine learning (ML) is hungry for content—and starving for structured data. Your taxonomy program is stuffed with specific domain-, industry-, and company-relevant structured data: Therefore, the machine learning team is, sooner or later, coming for your taxonomy. But not all taxonomies are well-suited to ML applications. How can taxonomists be good partners for ML—and not just data sources? Kasenchak outlines the kinds of taxonomies good (and bad) for machine learning and some advice on how to prepare for working with ML teams. Maddison reviews two machine learning use cases in place at Adobe, including how they train ML to auto-tag creative intent and how ML uses taxonomy to make content recommendations and product recommendations to customers.

Elsevier aims to streamline taxonomy production by creating a shared infrastructure supported by AI-based decision support tools. Vlietstra explains the components of this infrastructure, which include a candidate pool for incoming candidate terms. Here candidates are enriched with analytics and other AI-based decision support tools, such as a synonym suggestion classifier, a term categorization classifier, an ambiguity scorer, and a hierarchical relationship suggestor. Also hear about the taxonomy management system in which domain-specific branches are maintained, and the taxonomy compiler, which chooses from the modules to create taxonomies for specific products.

Celebrate the grand opening of the Enterprise Solutions Showcase. Enjoy drinks and light bites while visiting with conference sponsors.

In today's fast-paced and data-driven business environment, disruptive and innovative technologies like generative AI, automation, and machine learning are playing a crucial role in accelerating digital transformation across all industries. They are changing the way organizations innovate, operate, and work. With proof points like ChatGPT, generative AI will soon enough have a significant competitive impact on revenue as well as the bottom line. With the power of AI that can help people broadly synthesize knowledge, then rapidly use it to create results, businesses can automate complex tasks, accelerate decision making, create high-value insights, and unlock capabilities at scale that were previously impossible to obtain. Most industry research agrees with this, including one major study that recently determined that businesses in countries that widely adopt AI are expected to increase their GDP by 26% by 2035. Moreover, the same study predicts that the global economy will benefit by a staggering $15.7 trillion in both revenue and savings by 2030 thanks to the transformative power of AI. As a knowledge worker and KM leader, embracing generative AI technology can deliver a wide range of new possibilities for an organization, helping it ,to stay competitive in an ever-changing marketplace while achieving greater efficiency, innovation, and growth. Hear what CEOs are saying and get our experienced analyst’s insights on delivering the value of generative AI in the enterprise while navigating the challenges.

Taxonomies have evolved enormously over the past couple of decades, as have the methodologies and best practices for curating taxonomies and deploying them within the enterprise. Our speaker and his team help people organize, categorize, and discover enterprise knowledge. Clarke reviews some of the major challenges that Fortune 500 companies have had over recent years, along with the breakthrough moments with co-designed solutions using now out-of-the-box tools that any taxonomy practitioner can benefit from. The use-cases include challenges of complexity, such as the evolution from term-based thesauri to logic-bearing ontologies, and challenges of scale, such as developing high-performance search and APIs for taxonomies with tens of millions of entities. Clarke also looks at breakthroughs in autocategorization resulting from the challenges of transparency and explainability, as well as the impact that large language models are beginning to have on enterprise language models. When Synaptica started building taxonomies and taxonomy software, the only use case for them was to improve metadata quality and consequently search precision and recall. This use case remains a compelling ROI for building enterprise taxonomies, but today, the highly evolved state of taxonomies also supports totally new use cases that provide additional ROI, such as the automation of business processes and decision making, chat-based search, and the generation of new knowledge by inferencing over content-aware knowledge graphs.

Julius Caesar said, “Experience is the teacher of all things.” New talent is important but the value of experience is priceless. How do you capture institutional and skills-based knowledge to ensure the next employee gets off to the standing start? Minimize the costs of time and training when onboarding? Realize the benefits of new talent faster? The founders of Sugarwork share their vision and solutions for an area scantly addressed in the past, along with a success story, highlighting the transformation of a software company as it shifted the geography of its engineering team and used its structured tool to transfer knowledge from the more experienced team to the newly onboarded team. Time was tight, and Sugarwork needed a solution fast. And engineering knowledge is not so easy to capture and pass along. That’s when you pair the experts and the learners via a technology tool that enables structured sharing of institutional knowledge to capture the essential knowledge of the experts during a compressed period of time. Unlike tools such as intranets and collaboration platforms such as Slack, Microsoft Teams, and other stop-gaps, the Sugarwork platform facilitates the transfer of tacit knowledge—knowledge that lives primarily in the minds of individual employees—and is deeply ingrained in people's experiences. This becomes crucial in moments of transition or for companies facing a silver tsunami of retirements, divesting a business unit, or talent disruptions and that risk experiencing profound knowledge and productivity loss when experienced talent walks out the door.

Generative AI has the potential to transform customer and employee experiences—CX and EX. The question is how you can harness that technology for CX and EX transformation. Why is knowledge management foundational to success with generative AI? How do you integrate it with your current AI portfolio? What are the best practices to reap business benefits and mitigate risk? Roy shares his take on these questions using real world examples.

A primary goal of taxonomy and information architecture (IA) is to allow information to move across boundaries; from one place to another, from person to person. Using core concepts of information theory and his personal experiences with global organizations, Carlson unpacks real-world examples of the power of taxonomy and IA, including search, knowledge management, content management, and enterprise analytics. The core of these examples is to enable information flow between senders and receivers of information across languages, systems, interfaces, organizations—and establish better connections.

The Washington Post has a dynamic taxonomy system that highlights and displays the diversity of its compelling content. Ontology development and taxonomy integration interweave with content management systems development as part of product objectives. The content taxonomy systems team works side by side with newsroom journalists, producers, and editors to build and refine terminology for the taxonomy. The team discusses the impact of developing the enterprise taxonomy directly where content is created and the team’s work to refine the publishing workflow, taxonomy management systems, and auto-classification processes to meet project goals. Also hear about how analytics are used to glean valuable and actionable insights.

Taxonomists do their very best to make a well-thought-out taxonomy, in line with the content it is used for. However, how successful the taxonomy is in the end is also highly influenced by other factors, such as the quality of content and the quality of tagging. This might lead to an uncomfortable situation in which the taxonomy might be blamed for not delivering the desired needs. van Aalten shares her experiences on the most-heard issues that the taxonomy was expected to solve but, in real life, couldn’t. Learn how to address these matters and what actions you can take to avoid these pitfalls.

As ChatGPT has stormed into nearly every intelligent space with almighty power, we should expect a revolution to take place in taxonomy development and use. What risks and rewards might that revolution bring? Hear about opportunities and challenges posed by ChatGPT, how it can be used in taxonomy creation, and its potential for taxonomy management. Hear lessons learned on interacting with ChatGPT and how best to write ChatGPT prompts for taxonomy creation and management. Hear about two cases of taxonomy creation using ChatGPT and how human expertise was invaluable in verifying the AI assistant’s work. The panelists also discuss risks of AI-developed taxonomies and the path forward for this formidable technology.

Vinted, Europe’s major secondhand retail platform, recognized the need for a taxonomy and called in the taxonomists to build a program from the ground up. Lapin and Horan discuss their experience coming as experienced taxonomists into an organization without a history of working with information professionals. Where do you start? Where do you sit in the organization? How do you balance immediate problems, while also being forward-thinking. What constraints and limitations impact your approach, both technically and organizationally? Learn about how to build a taxonomy program with buy-in across the organization. 

Two will enter, one will leave. Ask your burning taxonomy questions to global subject matter experts, hear them argue both sides of the issue, and judge the winner. Fame and glory awaits the victor! Wahl guides our experts to debate the taxonomy community's most important, controversial, and entertaining questions.

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