Get the highlights and insights from Glimpse, the trend analysis which focuses on emergent generational and societal shifts, and learn how to respond to these changes. Understanding the global context teaches us what each person is facing with the world today and sets the emotional groundwork for every project. From this global context, Turner shares insights into how our minds have changed, how society has shifted in response, and where it is going through the eyes of generational and cultural evolution. Get ideas about what the future holds for knowledge sharing in our organizations. The eight important mega-trends that inform the world today, the disruptors are reinvention, non-linears, crowd-shared revolution, being human, data disruption, human +, a new visual-verbal culture, and human speak. Hear more and get ideas of how to prepare for this future.

Over the last couple of years, AI, machine learning, and deep learning have received unprecedented attention and are now reaching the top of the hype cycle. But today, despite many technology breakthroughs and success stories from early adopters, very few enterprise organizations are actually reaping the promised benefits. Sinequa is one of the few software platform vendors with customers deploying applications powered by AI models. Our journey in the field has taught us some valuable lessons, and there are some key takeaways we would like to share with you: a look at what pragmatic AI can do for you, bridging the gap between AI’s long-term potential and today’s capabilities, tips to identify the highest ROI use cases which can be addressed with AI models today, best practices to deploy these in production and start generating value. Get great insights and ideas from a KMWorld magazine award-winner and industry leader.

Knowledge graphs built on top of semantic technologies, supported by machine learning technologies, can become a paradigm change in how we deal with metadata management. Keeping track of what is going on in your data is the crucial momentum. Active metadata is a key element to achieve this. Traditional approaches do not work anymore—they are not adaptive, cross-application, and do not provide the semantic richness creating additional value from your data. You need a knowledge graph to specify your business rules and semantics. It is the bases for data enrichment, lineage, and impact analysis. Working in complex deployments requires metadata exchange in a unified, standardized way. Knowledge graphs provide better user experience and allow to fulfill specific workflow, security, and privacy requirements. Based on real business examples, our speakers illustrate how active metadata management works and provides more value to your data and, by that, your corporation.

Artificial intelligence, cognitive technologies, and related tools have the ability to fundamentally reshape knowledge management. As always with groundbreaking technologies and management systems, there is a mixture of some successes, lots of hype, and an emerging body of knowledge of how and where to deploy AI/cognitive for both quick wins and long-term transformational impact. Namir provides an overview of this rapidly transforming landscape and discusses how organizations can accelerate their AI investments to derive maximum value.

What are the current and future trends for the field of text analytics? Join program chair Tom Reamy for an overview of the conference themes and highlights and a look at what is driving the field forward. This year’s main new topic is knowledge graphs, which are being developed for everything from enterprise search to a way to build an enterprise platform to integrate all an organization’s data. We also continue the exploration of machine learning and rules-based approaches and how people are combining them to get the best of both worlds. The talk ends with a look at current and future trends that promise to dramatically enhance our ability to utilize text with new techniques and applications.

This talk describes the use of an enterprise knowledge graph as the semantic backbone of a text analytics application. Going beyond traditional hierarchical taxonomies, we demonstrate how we use a knowledge graph to enhance entity resolution, boost signal detection, and improve relevance scoring. We examine a use case where graph-informed tagging adds business value by surfacing connections between different facets of content and by driving personalization and user experience through precise metadata.

This presentation covers text analytics and text processing techniques used in creating several interesting text based knowledge graphs. One example is the Noam Chomsky Knowledge Graph which incorporates hundreds of articles and numerous books that Chomsky has authored about linguistics, mass media, politics, and war. Another example covers health effects for ingredients in foods and beauty products. We show how a combination of AI techniques and knowledge graphs can be used to transform text-heavy applications into an interactive response system that can be used by scientists, technologists, politicians, and scholars along with smart applications, intelligent chatbots, and question/answering machines, as well as other AI and data systems.

This session looks at how AI and data science may be able to shape the world of project delivery, particularly projects which have a high degree of complexity but only if mediated by human sense-making and decision support. Our experienced and popular speaker takes work from counter-terrorism (his DARPA and other work) and applies them to project management using a multi-methods approach. Get in on the ground floor and grab new ways of thinking about AI and analytics.

A panel of four text analytics experts answer questions that have been gathered before the conference, during the conference and some additional questions from the program chair. This was one of our most popular features last year, so come prepared with your favorite questions and be ready to learn!

We’ve been doing text analytics since the mid-2000s. What’s working and what’s not? Seth Grimes presents findings from a 2019 study that surveyed user experiences with text technologies, solutions, and providers. What capabilities are users looking for in text analytics products and services, and are they finding what they need? How has the market evolved—both demand and supply—and how should practitioners and solution providers stay on top of developments? This talk provides practical guidance from fellow users to help you extract the greatest value from text analytics.

At Indeed, we analyze petabytes of unstructured text in support of our mission to help people get jobs. We strive to go beyond keyword search and enable users to search by concepts relevant to their job interests, such as occupation, location, or salary range. Our talk focuses on entity extraction: how can it unlock meaning from big data and what are the best strategies for implementing it? Should you build an in-house tool, choose an off-the-shelf tool, or combine both of these approaches? We review lessons learned from implementing each of these strategies at Indeed. This presentation is targeted at practitioners who want to develop scalable information extraction systems and are interested in decision factors such as model performance, startup training costs, operator workflows, ongoing maintenance, and risk mitigating strategies.

Text analytics is a well-established discipline, yet many organizations don’t have dedicated text analyst roles. When they do, they arise from ad hoc needs and are embedded within specific functional units, often doing work which could be repurposed for other processes. One part call to action, one part proposal, hear ideas about who a text analyst is, where this role should sit in the organization, and what actions to take to create a text analyst role in your organization.

Increasingly, knowledge managers and product developers are looking beyond in-house proprietary data to external sources to provide context and critical information. For companies focused on innovation and research, important sources include scientific journal publications, specialized databases, and a growing body of open access data. This talk looks closely at two different customer use cases that take text and data mining (TDM) projects from start to finish. The first is a knowledge manager seeking to enhance content discovery and linking key research findings to in-house data. The second is a company focused on leveraging research content for product development. In both cases, TDM is applied to drive an efficient research and discovery process, saving time and money, and positively impacting revenue.

Text Analytics has emerged as a defining technology for enterprise transformation. Its promise is no less than a radical rethinking of how businesses organize their workflows and take decisions. When cognitive processes can be automated and scaled, the impact is faster processes and better insights, enabling professionals to focus on the highest added-value parts of their mission. Join this session to discover today’s “art of the possible” in NLP, based on examples of leading-edge analytics and process automation applications

A panel of four text analytics experts answer questions that have been gathered before the conference, during the conference and some additional questions from the program chair. This was one of our most popular features last year, so come prepared with your favorite questions and be ready to learn!