This interactive session starts by building a solid conceptual foundation for taxonomy creation and reinforces those concepts through audience participation. Starting with the basics, Hlava quickly advances to where and how to leverage taxonomies. This gives beginning and intermediate practitioners a good overview of the foundational knowledge for the more advanced sessions throughout the conference. Leveraging the taxonomy standards for the key components of a thesaurus, Hlava explores how those elements support the information needs of users from multiple perspectives and examines illustrative sites and behind-the-scenes solutions to see how a well-constructed taxonomy with a rich interplay of terms and synonyms leads to better information access. The workshop discusses developing a taxonomy that serves users, respecting their needs for specialized vocabularies. With hands-on activities, attendees gain insight into how a subject area can be viewed, described, and structured.This learn-by-doing session provides basic knowledge to create a taxonomy that suits your needs.

There are many challenges in managing different types of taxonomies.Taxonomies can range in size from less than 10 terms to more than a million, be in one language or 20, and have a simple hierarchy or complex ontological structure. It’s also important to remember that taxonomy is not a panacea to solve content management issues. It’s a critical step, but successful solutions go far beyond just taxonomy; if you’re not planning for LAT (“life after taxonomy”), then you might find that you’ve invested a great deal of time and money for a system that’s not ready to perform. This panel brings together experts who have tackled different taxonomies from a range of organizations. They discuss the unique challenges, different approaches, and expectations that can be helpful when working on different taxonomies, including how to transform your taxonomy from an academic exercise to a full-fledged vehicle for content management; how to design a taxonomy that meets real user needs; the relationship of taxonomy and metadata; the rigors of taxonomy governance; editorial guidelines and tagging strategy; and post-implementation tasks, analysis, and adjustments. Creating your taxonomy is merely the beginning; by the end of this session, you will have learned what it takes to cross the finish line.

This popular session facilitates a conversation with a panel of full-time taxonomists from the public and private sectors and the consulting world. The taxonomists discuss their career path, daily activities, and noted trends in the industry.The audience has the opportunity to ask questions, with answers and different perspectives provided from each panelist.

Taxonomy Development

Developing a taxonomy from existing sources of information is a good way to ensure your vocabulary is accurately reflecting the content you are classifying. Additional sources of content, however, can change the scope of the vocabulary and potentially create conflicts with the existing structure. How do you plan for what you don’t know? Are there tactics for developing a taxonomy that can grow and adapt to new information without causing a partial or complete overhaul of what you have already built? This session includes a real-world taxonomy development case study as well as helpful suggestions and best practices for designing a taxonomy able to adapt to new information sources.

Building a Multidisciplinary Thesaurus

JSTOR is a digital library of more than 1,500 academic journals, books, and primary sources. These holdings comprise a wide range of topics from the humanities to the sciences. Today, no one single thesaurus holds the terms to cover all these subjects. Known for quality metadata and quality images, JSTOR’s goal is to have a thesaurus provide for high-quality enhancement of its content. Hear about the steps and the issues involved in creating this thesaurus.

Tilling the Fields: Growing Old Taxonomies to Fit New Content

What does one do when one has 100-plus decade-old taxonomies that need to be repurposed? What if the taxonomies in question are highly idiosyncratic in both content and organization, but now need to play nicely in a wider, standards-driven environment? Education and reference publisher Cengage Learning confronted this issue in 2011, when the metadata team needed to harvest previously untended fields of course- syllabus-based taxonomies to feed new content and products. The crop yield from the legacy taxonomies wasn’t enough to implement this new indexing workflow in production systems and products. Would Cengage Learning practice slash-and- burn agriculture, or could it use the seeds of previous efforts to cultivate bountiful new fields? Members of Cengage Learning’s Metadata Standards and Services team take you through how they identified problems with their existing taxonomies, analyzed the problems and opportunities, and ultimately merged the old taxonomies into improved, discipline-based taxonomies.They share tips on taxonomy cultivation, and keep you from buying the farm.

Taxonomy Interoperability Standard

Taxonomies at last have a standard to support interoperability between taxonomies and other controlled vocabularies. The linking, multilingual, or interoperability of standards has been a holy grail for many years.With the passage of the ISO 25964 Part Two in 2013, the groundwork has been laid for further development in these areas. A brief overview of the standard and its relation to other current standards such as ISO 25964 Part One, revisions to Z39.19 and the updated British standard BS 8723 Parts 1–5 is provided. In addition, the ISO terminology standards used by the computer science community are discussed.

Taxonomy Modeling’s New Guard— SKOS-XL Concepts

This talk examines concept/label taxonomy design as represented by SKOS-XL (Extended Language) modeling, compared to traditional taxonomy design. It explores the differences between the two models and how each is able to handle specific design requirements, such as managing multilingual instances, synonymy, and distinct attributes. Relation to OWL and its variations and the use of “term ID” and its pros and cons within these standards are also discussed.

Discussion, Questions, & Answers

Building Enterprise Taxonomies: Lessons Learned

After almost 2 decades of building taxonomies for a variety of industries, applications, tools and organizations, there are many lessons to be learned and applied to today’s highly distributed and loosely connected information environments. Internal versus external applications, departmental versus business unit versus enterprise, application to structured versus unstructured, text versus rich media, back end versus front end, machine-applied versus human indexing, and a range of other variables for the derivation, application, and maintenance of taxonomies provide a rich backdrop for lessons learned.

7 Steps to EIM Taxonomy Success

The need to classify and categorize corporate information has never been greater. The proliferation of information channels, sources, and delivery platforms makes managing information a complex business challenge. Compounding this issue is the fact that, due to the increasing speed of business, information is growing at a rate that far surpasses standard institutional frameworks and controls. Information continues to be recognized as a key source of competitive advantage, and there is an increasing need for the business worker to access relevant information in a timely manner. Information by its very nature is dynamic, so attempting to set boundaries results in a cumbersome way to control the flow of information. The key to managing this information is to develop a way to identify, classify, and categorize enterprise information.This categorization allows for effective management of content throughout the information life cycle: capture, storage, retrieval, archival, and disposal. A systematic approach to taxonomy development goes a long way to ensure that the finished product, the corporate taxonomy, is relevant, usable, and provides value to the business. Hear the steps to develop your enterprise information management (EIM) taxonomy and the best practices to create the growth and outcomes for an ongoing taxonomy in the future.

Taxonomies for Program Management

Today’s organizational landscape, characterized by virtual offices, shorter tenure, global markets, and rapidly changing technology, makes effective information management a key performance objective. Common information management practices are needed for creating and storing resources so the information can be easily found and shared later. These practices range from simple file and folder naming conventions to more robust metadata schemas and tagging vocabularies. These taxonomies need to be readily understandable to employees without much, if any, training; they must be “natural” and “universal.” Some organizations are framing their information management practices as an integral part of over- all goals and objectives planning. In these organizations, taxonomies reflect the overall program goals of the organization. For example, every resource is related to one or more key business activities or tasks; and key differentiators, such as methodologies, are identified. In some organizations, creating, tagging, finding, and presenting information assets is a natural part of everyone’s daily routine, as natural as searching for a website or shopping for products in an online store. Finally, a taxonomy-based information ecosystem provides common and easy ways to measure and report on organizational performance as analytics and visualizations.While taxonomies are typically built to solve an information management problem such as browsing for content on a website, this presentation discusses how taxonomies are being used to 1) reflect the overall program goals of an organization; 2) be the framework for organizing, finding, and presenting assets from disparate systems; and 3) provide a common way to measure and report on organizational performance. Examples are provided from organizations that are using taxonomies to meet today’s program management challenges.

Enhancing Information Infrastructure Enterprise Taxonomy

Speakers walk through five aspects of developing complex enterprise taxonomies. 1) Scalability—a successful taxonomy is recognized for its dynamic nature and is integrated into a standard business workflow that accommodates business organizations, both internal and external areas of focus, geographies—regional and global differences, cultures—philosophical as well as geo-political differences among business organizational areas, and IT systems—providing integration points that are broadly consumable. 2) Inputs—use cases (requirements), lessons learned, and best practices. 3) Critical risks and success factors. 4) Data and information architecture—designing front-end data structure to facilitate downstream consumption with custom connectors, web services, indexing service for web-based tools, and reports with specified criteria. 5) Implement enterprise taxonomy as a management tool—aggregate like terms from diverse groups into consolidated lists that can be leveraged by the majority of consumers; using a confluence of filters and enhanced relationship management, deliver specialized taxonomy views to groups requesting taxonomy integration.

Challenges of Multipurpose Enterprise Taxonomy

An enterprise taxonomy is intended to be used by multiple groups within a company and to bring all the benefits and efficiencies associated with standardization across the enterprise. Meeting needs of diverse groups and synchronizing different conceptualizations and terminologies are known and often discussed challenges with a more-or-less established arsenal of solutions. But is there a point at which the essential purposes of taxonomies used by different groups are so different that challenges of synchronization and reuse acquire completely new dimensions? This talk presents challenges faced by a large company with huge amounts of information and generally decentralized information management that has seen work on shared vocabularies in different organizational units since the late ’90s. Three major efforts which have survived through 15 or so years of coping with organizational restructuring, staff fluctuation, changing strategies, technologies, and budget priorities have recently converged, and options for integration are currently being explored. These three sets of controlled vocabularies have substantial overlaps and an increasing number of shared stakeholders, but they have been developed for essentially different purposes, come from different communities, and follow different global standards and governance approaches, in addition to all being shaped to some extent by their long and winding histories and constraints of specific tools. The session includes analysis of the problem, selected approach for addressing it, and lessons learned that can be translated into best practices to be followed when developing enterprise taxonomies.

Integrating Enterprise Taxonomies With Local Variations

Balancing the need for a standard taxonomy for the entire enterprise and the desire to support local variations is one of the basic problems of enterprise taxonomy development. In addition to taxonomy structure issues, there is a large change management component. Trying to impose the same standard vocabulary on every group, while often attractive to enterprise taxonomists, fails to adequately reflect real local needs. This talk is based on a recent project at a large international financial institution which dealt with a somewhat fragmented environment using an enterprise taxonomy implemented with a text analytics tool, a secondary enterprise structure, a special topic taxonomy, and multiple knowledge management taxonomies managed by several KM networks.

Discussion, Questions, & Answers

Search engines, distributed processing and content processing pipelines are not new. However enabling technologies of mature search engines, powerful content processing pipelines and cheap distributed processing are coming together to empower a next generation of information access, analysis and presentation much closer to the holy grails of knowledge management. Hear from the founder of Search Technologies how modern search engines are currently being combined with powerful independent content processing pipelines and the distributed processing technologies from big data to form new and exciting enterprise search architecture, delivering results only available to the biggest companies with the deepest pockets in the past.

Pattern Analysis & Categorization: Big Metadata Toolkits

The exploding volume, complexity, and velocity of structured and unstructured data and their interactions presents challenges and opportunities to derive valuable insights. Among the challenges of managing massive datasets are gathering, validating, preserving, analyzing, and maintaining linkages from those analyses to the source dataset. Identifying patterns in datasets using information retrieval methods and writing out the results as metadata are well-established information management processes that should be adopted by organizations working with today’s Big Data sets. This presentation provides an overview of pattern analysis and categorization methods, including keyword and regular expression matching, business rules, pattern categorizers, entity extraction, and trained categorizers that are the key building blocks of analytics toolkits for big metadata applications.

Using Example-Based Auto-Categorization to Tame Big Data

There are at least two schools of thought regarding Big Data. Part of the organization wants to take advantage of it.The other part views it as “Dark Data”—undiscovered, unanalyzed, and unreachable without the proper analysis tools and skills. It’s both. Concept-based auto-categorization,which automatically categorizes documents based on their actual content, not keywords or terms, is the fastest, easiest, and most repeatable way to pinpoint only the most important documents and emails among libraries spanning millions of files and messages. It is an established standard in legal e-discovery and U.S. intelligence, having proved defensible and highly scalable. Learn how companies are beginning to step up to big data analytics using example-based auto-categorization in order to take advantage of all their data, no matter where—or how—it resides. Analytics for Big Data can bring great value to many business applications—social media, market analysis, internal information analysis. It is also being applied toward governance issues and regulatory and legal compliance matters. We’ll also look at empowering the entire information life cycle; how the “infomediaries” gain business advantages; and why traditional search engines have not been able to keep up.

Benefits of Integrated UX-IM Design

Customers today face market-driven design challenges that span multiple content formats and sources, multiple application platforms, and multiple user platforms. These challenges often require the services of two types of designers: user experience (UX) designers and the information modelers (IM) of taxonomies and metadata. An integrated UX-IM design team is one approach to accelerating solution implementation. When approaching the overall design with a collaborative team, the project can have a common methodology and shorter design cycles.This presentation examines how collaboration between these teams ultimately benefits the entire design cycle while resonating with executive sponsors.

Ensuring Consistent & Accurate Tagging: Interface Design & Metadata Application

As taxonomists, we spend a lot of energy designing vocabularies that are browsable and user-friendly, but we often overlook the system interfaces that taggers use to access and apply taxonomy terms to objects. The irony is, even the best vocabulary is of little value if a poorly designed tagging interface impedes a user from tagging consistently and accurately. This presentation looks at how tagging interfaces impact the metadata application process, exploring examples from content management systems, product information management systems, and crowdsourcing sites from around the web. It discusses how interface design can influence data quality, findability, and the long-term viability of a taxonomy project and concludes with some high-level principles to consider if you’re evaluating, building, or revamping a tagging interface.

Applying User Research to Designing Info Models

User experience design isn’t the only practice that can benefit from user research. Exploring the way users natively organize information in their minds gives taxonomists a means to precisely identify and define information structures, as well as a powerful guiding mechanism for difficult information model design decisions. This talk demonstrates how to construct an effective research plan that blends quantitative and qualitative methods to explore and analyze user mental models and how to transform research insights into viable, flexible, and sustainable information models.

Utilizing Ontologies for Taxonomy & Content Organization

This presentation focuses on the design and implementation of ontologies and how they are leveraged to implement taxonomies and provide better content organization. It demonstrates through case studies how this implementation improves unstructured content search and retrieval. Case studies illustrate how this approach to content organization has improved “findability” and reuse of content and knowledge in several organizations. Along with examples of ontology and taxonomy adoption, an underlying view of the card sort results, keyword, and controlled vocabulary building used to meet the business expectations of the KM solution are shared.

Enhancing Searches With Taxonomies & Semantic Tech

We’ve all seen how the major search engines sometimes second-guess—often correctly—what we meant to search for; taxonomies using standards-based semantic technology can help your own applications do this and more.While semantic technologies rarely try to store the complete meanings of words, data about the relationships of words and phrases to other words and phrases (for example, “broader than” or “alter- native label”) can often store enough semantics to automate search enhancement.When this data is stored using the W3C SKOS standard, it can more easily be aggregated, queried, and used by a variety of tools. Because SKOS is based on RDF, the growing amount of publicly available RDF data about terms and term relationships can be an especially big help to drive improved searches with your own systems. This talk looks at how taxonomies based on semantic technology can help with focusing and augmenting searches, correcting terms, disambiguation, and using other vocabulary metadata sources to improve the data driving your search enhancements.

Developing a Semantic Search Application: A Pharma Case Study

Adding semantics to search can be a daunting task since it involves dealing with language (messy and not really an IT core competency) and relatively new technologies such as text analytics, and, to work well, requires an interdisciplinary team. Given the complexity and uncertainty of developing semantic search, it usually makes sense to start with a small, focused pilot or POC.This is what one pharmaceutical company decided to do. This talk describes an initial pilot carried out by a varied and diverse team using three different search and text analytics products. It discusses the pluses and problems this diversity caused, what worked best, and what didn’t work so well and covers the key issues and approaches that were needed for success.