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Discovering Knowledge Insights with Cognitive Computing


Cognitive computing, machine learning, and artificial intelligence have the ability to transform knowledge management in the enterprise.

Strategic KM platforms using AI to discover insights; AI-powered search that leads to enhanced analytics and discoverability; and knowledge bases that are made more effective with metadata are key components of how cognitive computing can re-imagine KM within your organization.

KMWorld recently held a roundtable webinar with David Seuss, chief executive officer, Northern Light Group; Scott Parker, director of product marketing, Sinequa; and Bob Kasenchak, director of business development, Access Innovation, who discussed what it means to be "information-driven."

“Employees spend 1.8 hours every day – 9.3 hours per week, on average – searching and gathering information,” according to Parker who referenced a McKinsey Report - Time Searching for Information.

Data is coming in from a plethora of sources, leading to a sort of “digital hoarding,” Parker explained. But because of constant change, whether through updates, new sources, or access changes, data is only good a sits interpretation.

To become information driven, Parker recommended Sinequa’s suite of products. The presentation forms the user experience’s building blocks. It consists of a UI framework, business API, and analytics API.

Sinequa offers smart connectors for any data source and converters for any data type, along with natural language processing, semantic extractors, and text mining.

We are approaching the era when users will no longer search for information, Seuss explained.  Search is evolving to have an in-depth understanding of the searched material and the ways of knowing in the user’s knowledge domain.

Using machine learning, search is now able to distinguish not just what is relevant but also what is important, summarize the important ideas in a document, across documents, and across sources, and learn what each user cares about and find the relevant material without being asked.

Northern Light can also help KM users jump into the fray. Northern Light uses latent semantic Indexing to model user interests and find recommendations, Seuss explained.

The types of business content machine learning can be used for include:

  • All textual document types (Word, PowerPoint, PDF, HTML, XML)
  • Business and technology news
  • Syndicated market research
  • Primary market research
  • Technical journal articles
  • Conference presentations

Another aspect of machine learning and how it can help KM is within the realm of taxonomy, Kasenchak said. Taxonomy is assigning things to a list of pre-determined categories.

An archived on-demand replay of this webinar is available here.


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