People are at the core of knowledge-sharing—the key to high functioning organizations. In John Seely Brown’s words, “We participate, therefore we are.” New and emerging technology can only enhance learning, sharing, and decision making to create successful organizations. Join our inspiring and knowledgeable speaker as he shares his view of the future of people and tech working together to share knowledge and create winning organizations.

Text analytics and auto-categorization tend to present themselves to the world as esoteric disciplines supported by complex expert systems. Users are immediately confronted by a jargon-wall built with terminology from computational linguistics, such as “tokenization,” “lemmatization,” and “NLP.” At past KMWorld/Taxonomy Bootcamp events, some practitioners who are attempting to get started with auto-categorization projects have voiced a common set of frustrations. Categorizing content shouldn’t require an advanced degree in linguistics. Categorization rules should be simple and transparent. Rules development and taxonomy development should be coextensive rather than separate activities. Rules should be easy to edit, and it should be possible to understand quickly and precisely how changes to taxonomy and rules impact document categorization. This talk explores these issues from a design and user-experience perspective. It outlines a manifesto for demystifying text analytics and for simplifying the process of auto-categorization. The manifesto is aimed at a constituency of content owners and taxonomists and hopes to help them take ownership of the categorization process so they can better control the search and discovery experience for their end users. 

Recently, NPR’s Research Archives & Data Strategy (RAD) team has moved away from their largely manual metadata creation process and incorporated auto-categorization in their digital archiving workflow. In particular over the past year, the RAD team has worked to expand and refine their existing vocabularies to meet the needs of users across NPR and to enhance the accuracy of the auto-categorization. This talk highlights the challenges and lessons learned to date. We discuss the challenges of implementing and fine-tuning the taxonomy management and auto-categorization software and how new tools have had an impact on the workflows of all RAD team members.

Hear about how HBO created its own metadata standard through adaptation of multiple industry metadata standards. Our successful solution is a team effort utilizing taxonomy and ontology expertise, combined with taxonomy data governance, project management, and the right metadata tools. The use of international standards enables the creation of authoritative vocabularies and eases communication between data points. Learn in detail about how to manage taxonomy projects and the type of taxonomy and ontology expertise needed to capture and document the efforts to ensure successful adoption. Hear tips and tricks on how to take existing standards and make them your own, saving time and effort.

Taxonomy development does not scale: companies rarely invest in the human resources needed to build and maintain taxonomies. In 2015, the new enterprise knowledge management team at USAA was tasked with improving the quality of the enterprise knowledgebase. A temporary solution for lack of resources was developed for the pilot, using SMEs with business units as part time taxonomists. The team was so impressed with the results, we expanded the “temporary” solution to our enterprise strategy. Hear how USAA established a new role within the business units to help in optimizing knowledge findability and the taxonomy development process.

Tasked with helping its 2017 Annual Meeting attendees sort more than 200 sessions by personal interest, the American Society of Clinical Oncology (ASCO) leveraged its taxonomy to produce targeted program recommendations. The Session Recommender leveraged existed profile data supplemented with additional information supplied by the user via a short questionnaire to craft individual taxonomy profiles, which were used to generate recommendations. During this session, Travis will offer an overview of the project, including design considerations and challenges, as well as how ASCO intends to use it as the basis for future personalization efforts.

AI and deep learning are hot topics these days, but when you look more closely, most of the advances have to do with image and pat-tern recognition. Taxonomies, on the other hand, have mostly to do with much messier things—words and their multiple meanings. Do these two fields have any common ground? Reamy looks at some surprising ways that companies are learning to take advantage of the strengths of each. With multiple examples from recent projects, he reveals some of the recent successes and roadblocks of combining AI and taxonomy. The next speaker discusses how the business environment is primed for AI and how organizations are learning to incorporate automation in search results as well as in search suggestions. Where would you rank your organization’s competency in automating taxonomy? For most, the answer is simple: It’s not where it needs to be. Historically, taxonomy development has been a manual process. It doesn’t need to be any longer. Hear how three blue chip companies are taking an active approach to the problem, automating the analysis of stakeholder needs and adjusting taxonomy output on-the-fly.

EY’s knowledge architecture team is responsible for developing and maintaining the taxonomies that underpin its core knowledge, service delivery, and collaboration applications. The team currently manages 45 taxonomies, comprising more than 8,000 terms, as well as an auto-classification program. Establishing governance processes is relatively easy; ensuring adherence to them is more challenging, particularly for EY, which employs 230,000 people and operates four core lines of business in 150-plus countries. This presentation describes EY’s overarching taxonomy design, governance model, and taxonomy management processes, including our auto-classification approach. It shares success stories and highlights pitfalls to avoid with a global enterprise taxonomy. Just over a year ago, National Geographic’s taxonomies were only in American English and were U.S.-centric. They now are in 11 languages and counting, a key part of the standard infrastructure tying together National Geographic’s global presence with text analytics support for all languages. This talk is the story of its transformation, and Fulvio shares key lessons and insights gleaned along the way.

Just as humans thrive on nutritious diets, high-performance applications demand well-fed taxonomies and ontologies: rich, up to date, and organically tied to the reality of your data, rather than a simple schema. But spoon-feeding your ontology with facts (or triples) one-by-one is hardly a plan when your knowledge is dispersed throughout a disparate array of information assets. That’s where cognitive technology, and specifically natural language processing, comes into play. Based on your schema, it harvests the instances of your object classes and relationships that actually occur in your content, capturing a wealth of knowledge that makes and keeps your ontology healthy. This session showcases concrete examples of how to successfully implement cognitive technology to feed your ontology and power your information application.

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 will compete to answer your questions with insight, entertainment, and perhaps even controversy! The best questions (as voted by the audience) will bring home prizes!