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Successful Data Discovery with Taxonomies and Semantic Analysis

It can be challenging to gain insights and make strong decisions from siloed, unstructured, language-based enterprise data. Teams that combine personal and open-source approaches tend to spend more time managing their technology stack than using that knowledge to create value. Gaining control over language assets such as documents, emails, reports, and webpages can help teams to build more efficient semantic search, intelligent applications, and customized knowledge bases. This can be done through a combination of symbolic (rules-based) and machine learning approaches to provide the highest degree of accuracy, explainability and flexibility. Join AI and knowledge discovery expert Christophe Aubry to hear several real-world natural language examples where Hybrid AI is used for successful data discovery. In the webinar you will learn how to: • Identify relevant concepts and topics by applying automatic semantic analysis • Improve knowledge discovery and natural language applications by building your own knowledge graphs • Automate document analysis by semantically classifying large volumes of unstructured data with taxonomies

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