Upgrade to a Platinum Pass for your choice of two preconference workshops or access to Taxonomy Boot Camp, a co-located event with Enterprise Search & Discovery 2022. Workshops are also separately priced.

Join us for the Enterprise Solutions Showcase Grand Opening reception. 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.

Building on early knowledge mapping work, Snowden discusses how small things create resilience and sustainable change while large initiatives are more all or nothing. Typically based on an ideal future state definition, large initiatives produce a more linguistic conformance approach than real change. It’s more difficult to allocate blame with smaller initiatives, and the right people usually get the credit. If something small fails, we are likely to learn from it. Getting to the right metaphor is important. The estuarine metaphor is a physical image that people understand. It’s not all about linear flows; tides matter. Cynefin ideas resonate between the familiar and the novel and produce a body of material that can be reused or adapted. Working with nautical charts to create a real-time strategic representation for organizations with the ability to initiate surveys in real time, Snowden links to the future of C2 command in military terms. He emphasizes that any approach to strategy needs to be dynamic and non-linear, allowing for fractal or, maybe better, holographic representation (break it and the picture is still there in the shards) for a fluid integration of strategy with operations and tactics. We need new ways to express strategic intent--enough structure for direction while maintaining spontaneity and adjustments. This links in turn to the balance of rules and heuristics and critically ideas of distributed, not delegated, authority. Gain new insights and ideas from Snowden!

For over 25 years Synaptica has been helping clients to Organize, Categorize and Discover enterprise knowledge. Individually and collectively these three tasks require people from different teams and departments to collaborate, to understand each other’s roles, and to share knowledge. The common goal is to make search more relevant and knowledge more discoverable. Achieving this goal requires the coordinated effort of content specialists, information scientists, data scientists, and computer scientists. In this short talk Clarke will discuss how to promote cross-team collaboration that pulls together stakeholders responsible for content, metadata, taxonomy, databases, information architecture, and search.

The Great Resignation affects organizations in many ways and contributes to the loss of critical knowledge. As employees leave, much of their expertise goes with them, resulting in knowledge gaps. Meanwhile, current employees don't have the resources or past knowledge to do their best work and perform at the top of their profession. If unchecked, employees give up on trying to find and leverage institutional knowledge, and organizations lose their competitive edge and begin to atrophy. Intelligent search is the key to unlocking organizational information and surfacing insights that are crucial to success in today's market. CMSWire conducted a market research study to evaluate the perceptions that affect KM and how important content can reach employees across the enterprise. Come hear surprising and insightful results from the survey as well as how to capitalize on knowledge assets to minimize turnover and maximize performance; how to deal with disconnects, evaluate misalignment, and explore paths to fix knowledge gap challenges; and how the latest advances in search provide better relevance than ever before for critical stakeholders.

In knowledge-intensive workflows, where decisions have to be made continuously, users benefit from accurate suggestions. In recommender systems, the business objects (documents, experts, products, suppliers, etc.) that best fit the respective context are automatically brought into the spotlight. The possible applications are many. Blumauer provides a comprehensive overview of possible uses of recommender systems and why they should be central building blocks in the digital workplace, especially in enterprise information systems such as knowledge and content hubs, customer experience platforms, or support systems. He shares examples and demos applications in the environment of HR management, drug approval, and technical support for software or legal. Hear how to establish a recommender in your own environment and move your organization’s digital transformation ahead.

Compared to traditional web search, enterprise search receives many fewer clicks, making it a struggle to understand user behavior. Measuring the quality of search results thus defaults to input from expert users and content creators. Eric Pugh introduces Quepid, an open source project for supporting human-rated testing programs, and explains how it puts a number on the classic question, "How good is my enterprise search quality?” Lucy.ai is also concerned with search quality. Lucy's technology plays a critical role in prioritization with her search capabilities that empower team members to share knowledge resources and insights, collaborate more effectively, and operate in a do-it-yourself environment.

Before you can improve search and findability in your organization, it's essential to do a reality check. Based on real-world examples from Molnar's international practice, this session shows you how to do a systematic review of your Microsoft 365 content and information architecture to surface where the gaps are. She then leads attendees through building short-, middle-, and long-term action plans to improve findability. To go beyond pure Microsoft technologies, Ulmer provides a case study of enterprise search within a large French bank using Datafari Enterprise Search,which is able to search and find from many diverse sources, including, but not restricted to, O365.

Enterprises rely on information intelligence to find relevant, contextual, and precise information from disparate and distributed silos. Traditional search, however, delivers a poor experience, making a paradigm shift necessary. Deshpande explains how machine learning can support natural language queries with content comprehension in multiple domains, and automated content ingestion at large scale. Persistent Systems, through its Kendra Engage360 solution, delivers intelligent search within a number of SaaS applications. Krishnan addresses what he sees as the Achilles’ heel of enterprise search: the amount of time to determine the relevance of search results. He discusses the traditional shortcomings of enterprise search, the limitations of AI for such applications, and how the human brain can solve the “last mile” by reducing the review phase to a fraction of conventional search.

Web content is unruly, misbehaved, messy, scattered, and badly formatted (if formatted at all). It’s also informative, insightful, timely, and crucial for business analysis and technical research. Web content that is “free” and “openly accessible” to the public is a fast-growing content category but presents copyright infringement pitfalls. Enterprises have a significant task on their hands when it comes to populating knowledge management portals with web content. Seuss presents strategies for addressing the various issues associated with incorporating web content into enterprise knowledge management systems, with an eye toward maximizing the accuracy and value of the content and limiting potential organizational liability. Prizzi calls our attention to the future of machine learning and its influence on content presentation and product discovery experiences. By using intelligent, enterprise search with AI like semantic understanding and machine learning, the data gathered from search behavior can dramatically improve and personalize experiences while providing significant insights for companies.

At EY, practitioners spend a significant amount of time looking for specific slides to reference or reuse in client pursuits or presentations. Allowing them to search for individual slides directly within PowerPoint saves significant time, because they don't need to download and sort through large presentations to find a single slide. Find out how EY implemented this time-saving feature and the lessons learned along the way.  Panovski explains that, although digital workplace offerings in the cloud are used more broadly than ever, the need to find and access knowledge is even more important. He outlines the possibilities for search within the digital workplace to provide the most relevant information to colleagues.

This keynote panel looks at the connections between the conference streams of knowledge management, taxonomy work, text analytics, and search. When it comes to implementation, there needs to be an understanding of what each discipline contributes. What is their common ground? How can and should they be orchestrated? Taxonomy work is founded on a suite of methodologies, frameworks, standards, and technologies to organize information for general access and use. Search tools need to reflect how taxonomies and controlled vocabularies can make search smarter. There has been an increasing convergence between taxonomy, search, and data science expressed in the subdisciplines of data analytics, text analytics, machine learning and AI. KM sets the strategic purpose as well as technology implementations using taxonomies, search, and text analytics tools. It identifies and characterizes the contexts of information use so that KM initiatives and technologies can serve practical needs. These three strands do not always interact well. Knowledge organization systems can be be too complex and impractical and are often implemented in ignorance of basic information science principles and technology. AI/machine learning applications are often implemented without rigorous conceptual underpinnings and may not easily scale across multiple working contexts. Hear how information science can combine insights from KM when the KM understanding is not present and from data science to develop more effective, more sustainable knowledge organization architectures.

Nearly 80% of enterprise data is unstructured and language-based, making it much less accessible. Join this session to discover the three keys to successfully turn your language assets into data to enhance analytics and empower your team to make better decisions. Start learning how to analyze your complex documents, extract language data to accelerate intelligent process automation, and the “signal through the noise” to understand market and customer insights. Get three keys to natural language processing (NLP) that create business value and a head start on projects with customizable, pre-built knowledge models. Learn how to simplify, accelerate, and improve your natural language projects and hear about some successful NLP use cases that deliver quick value for any business.

Increasing content volume makes it hard for knowledge consumers to find all the information they need to make a decision. Whether for prospective customers or employee and partner enablement, poor information findability results in inefficiencies and lost opportunities. Intelligent content which is structured and has metadata with a framework of taxonomy in place can be transformational to user experience. Taxonomy features in content management have long been siloed and limited in scope. Organizations can break away from old standards and by adopting global standards, they can enable knowledge models to be truly integrated across the whole enterprise, and even beyond, to smooth reuse of industry standard taxonomies for efficient data sharing and governance. Learn how structured content together with the power of modern taxonomy and dynamic content delivery can bring enormous benefits to the end users to drive improved customer experience and manage information more efficiently.

According to our survey of over 450 agents, 63% of contact center agents say that customer queries are increasing in complexity. As self-service gets smarter, it is leaving only the complex questions for agents to handle. This means all contact center agents need to be able to handle routine informational and transactional queries as well as situational queries that only SMEs (subject matter experts) used to handle. In other words, all agents need to become super-agents. How can a contact center make it happen? The answer lies in modernizing knowledge with a Knowledge Hub. Learn what it is and how forward-looking contact centers are leveraging it to create “wow” in agent and customer experiences.

Ontologies, taxonomies, and controlled vocabularies (OTCVs) can help researchers develop their search strategies and help refine search results. However, there are many overlapping and intersecting OCTVs in each domain. Does it make sense to use all available tools, or is the use of every available tool counterproductive? Goldstein shares his opinion in the context of Nebula, ResoluteAI’s enterprise search platform. Puzicha looks at taxonomies by machine learning in enterprise search, noting that automated document classification works well.

A good enterprise search engine is vital for knowledge discovery and collaboration. Search is becoming more of a middleware to many applications in MITRE, which leveraged open-source solutions to enhance an enterprise search application and in-house personalized recommender system. This involves an API that acts as an intelligent proxy, extraction of latent topics, using the doc2vec model from Gensim for a content-filtering algorithm, and analysis of the success of the recommender solution. We explore the latest language models from Hugging Face repository and how they address some of the challenging problems in NLP.

This panel of graph technology experts discusses the role of graphs in the enterprise search world, going beyond the marketing speak to concentrate on real-world examples. With a focus on what actually works and how graph technology adds value by enriching unstructured data and connecting disparate elements that might otherwise be unknown, the panel confronts the myths and realities of today’s search world.

Many search applications are becoming more like question-answering systems where users expect to type in a natural language question and get an answer. Traditional keyword search approaches might just yield a list of page or document results where the exact search phrase can be found, but a more sophisticated technique, called Extractive Answers, allows developers to analyze the query and extract actual answers from existing content. This session explains and demos how Extractive Answers works and how it is used in examples, including Google Search. Lewis describes the role of intelligent search, NLP, and unified search to enhance enterprise search and discovery.

This is not your father’s enterprise search. AI-based technologies, the prevalence of search outside the enterprise, and user expectations have radically changed how enterprises implement and maintain their search operations. 

Srigiriraju notes that enterprises face many challenges providing real-time information to various teams to enable data-driven decision making. Data dashboards and analytical tools present an opportunity to provide necessary data and remove data silos. Pugh reviews the technical innovations in search that were front and center at Haystack Berlin, concentrating particularly on vector search. Cizmar explains that, from Hadoop and Lucene until now, search has driven the next-generation user experiences. With the shift away from keyword-based search to AI-based technologies, a radical shift has begun to emerge. He reveals the five features you should be prioritizing on your roadmap. 

Artificial intelligence and machine learning (AI/ML) are becoming increasingly influential in mainstream KM, whether it’s in powering digital transformation; automatically connecting us to relevant people and content based on similarities of context and activity; extracting concepts from documents, images, and audio-visual files; or finding meaningful patterns in very large datasets. The very complexity of these tools renders them opaque to business users. Too often, people are given AI capabilities and are expected to use them without the necessary safeguards. How are we to know whether the tools we are deploying are fit for our particular purpose and that they do not carry hidden biases or errors? Klein, a research psychologist famous for pioneering in the field of naturalistic decision making, shares five accessible and practical tools from his new book for exploring, understanding, and explaining the boundaries, constraints, and AI/ML applications so that we can direct their uses more effectively, safely, and securely.

It’s important to put employees at the center of work and create a culture where they can thrive through knowledge and expertise. Our lively and knowledgeable speaker explores how customers and Microsoft itself have implemented Viva Topics. She discusses best practices for getting deployed, garnering adoption, and scaling across organizations. The focus is on the practical application of how People + AI work together to generate a useful, engaging, up-to-date knowledge base. Get lots of tips and ideas ramp up knowledge sharing in your organization.

How many times have you heard that phrase? Join our popular and experienced thought leader as he discusses new Google-like, AI-driven experiences that users expect these days in search applications. These applications are driven by technologies like knowledge graphs, vector search, and advanced natural language processing, many of which are available as open source. Hear how these technologies are delivering want users want.

With new machine learning methodologies and innovations taking the search industry by storm, designing a user experience (UI) that aligns not just to relevant results but to predictive and personal experiences is the way of the future. Think about the users--they don’t want to spend their time searching, they want to find their files and get back to work. Relevant search results in an intuitive, easy-to-navigate UI are what the users care about. Visualize what you want the user to experience, then wrap data and services around that, which means you’ll get closer to an ideal transformational outcome for your users. Join our experienced technologist and leader: Get tactics and techniques that create a knowledge management experience that is based on user goals--not system capabilities. The time to transform the way your users discover knowledge is right now!

Karen Passmore presents a case study on a search experience for RecoverX, a startup that develops evidence-based medicine (EBM) augmented intelligence (AI) technologies to support and enrich physicians’ decision making. The RecoverX application provides evidence-based insights at the point of care and next-best-action suggestions for decision support in real time and in context, using data from clinical charts, real-time patient conversations, domain ontologies, and test results. Rong explains how Microsoft Search applies state of the art deep learning technology to deliver the most relevant results and modern experience to enterprise search. She covers how Microsoft built out its Semantic Fabric, leveraging Microsoft Turing deep learning models.

Ravi and Ajmal explain how to accelerate media exploration with intelligent search using Amazon Kendra. Increasingly, key information is produced as podcasts, video blogs, and human-generated unstructured documents. Amazon Kendra, a fully managed intelligent search service, enables you to extract accurate answers faster from your information source and allows hooks to enrich indexed data using Amazon Transcribe and Amazon Textract for enhanced image and media search.

Do you need to deliver enterprise search across information in SharePoint sites along with other content repositories and possibly even some of your structured data? Do you need your enterprise search to understand your company’s jargon? Do you want search results to return organized facts as opposed to only lists of hopefully relevant links? Knowledge graphs can help. The term “knowledge graph” was coined by Google when it recognized the need to enrich search algorithms through leveraging a curated knowledgebase of facts. In the enterprise, knowledge graphs can include controlled vocabularies, reference data, and other commonly used terms and entities as well as connections between them. Enriching and categorizing content using terms from curated vocabularies, makes it more findable. An enterprise knowledge graph can create a foundation for linking structured and unstructured sources based on the common terms. It can capture commonly asked questions, drive recommendations, make natural language processing more powerful, and more. Join us to learn how using knowledge graphs can help in automatically enriching your unstructured data and improve users’ ability to find relevant content and facts.

KMWorld magazine is proud to sponsor the 2022 KMWorld Awards, KMPromise & KMReality, which are designed to celebrate the success stories of knowledge management. The awards will be presented along with Step Two’s Digital Awards, where you get a sneak peek behind the firewall of these organizations.

Advanced AI is poised to uncover another view of the future through horizon scanning, and search is at the heart of horizon scanning. Rather than predict the future based on current and historical data, horizon scanning focuses on the multitude of small events–or tiny signals–happening now as indicators of potential change. Gross covers the overall concept of horizon scanning, how AI powers the foundational search, the search creation, and how to put this powerful knowledge into practice.

Defining the scope of enterprise search is a challenge for companies that have extensive product documentation, such as software companies. Searching product documentation needs to be part of a larger knowledge hub, but this also needs to be enabled separately for external users. The different front-end applications can pull from different content repositories, but it may be more practical to have a single taxonomy and search technology. Hedden presents some tips and issues in setting up a combined knowledge hub and help pages search at a software company. Following a brief description of the technology used, she addresses the issues of having separate or combine taxonomies, search refinements/facets for different audiences, search suggest topics with context, and recommendations for related content.

What’s ahead for enterprise and discovery? What technology delights await us? How have remote working and the Great Resignation reset affected search? What new strategies and approaches will we see in the coming years?

As the information age rapidly accelerates, organizations are challenged with decreasing employee productivity, high employee churn and changing market conditions. We will highlight the importance of integrating data and knowledge to deploy an effective AI strategy as a competitive advantage. Investing in the correct knowledge infrastructure to support AI initiatives is crucial to maximize existing technology investments, improve employee productivity and increase the engagement of your customers. Modern knowledge infrastructure transforms knowledge into the answers that your customers need and that both humans and machines need to operate effectively in the modern workplace. Learn why investing in the correct knowledge infrastructure is the most valuable investment that you can make to prepare your organization for the future of work.

Join the members of Knowledge Cast, the number-one ranked podcast on KM. Wahl and his guests provide insights and inspiration via a live-stream episode of the podcast. Hear some of the key ideas and innovations shared at this year’s KMWorld as well as what our panelists are seeing within the rapidly changing field of KM.