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What sort of solutions are available for content-based collaboration and "knowledge workflows"?

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If your team has a mountain of data that needs to be searched and organized and then used in a collaborative knowledge workflow..  the answer may be a "semantic knowledge exchange" . 

The next version of TrustNetMD's product will be a knowledge Exchange that is fully integrated with a text analytics engine. The result is that doctors, researchers, medical workers, device makers, pharma teams,etc., can collaborate to solve problems in the virtual Knowledge Exchange while working in the context of large, complex bodies of semantically organized content. 

Key capabilities of the Semantic Engine: 

  • Machine Learning
  • Natural Language Processing
  • Document Understanding
  • Graph-based Reasoning
  • Bayesian reasoning
  • Belief networks
  • Document Classifiers
  • Business Intelligence data mining

Unstructured Data Analysis

 

Capabilities include semantic analysis of unstructured or “semi-structured” text content such as web pages, documents,  social media, research papers, reports, medical records, work logs and forms,  RDF triplestores, and any free form text:

  • Sentiment analysis (evaluating the sentiment of the author of a document)
  • Named Entity Recognition (parsing out significant references to real world objects)
  • Document classification (different ways of cauterizing and classifying documents)
  • Relevance recognition (determining how relevant a document is to a given topic)
  • Paragraph Gisting (extracting the core meaning of a paragraph)
  • Ontological search (recognizing similarities from context)
  • Semantic filtering (recognizing what a reference is about from context)
  • Auto-generation of tags to add to the search space of user-generated content
  • Auto generation of links between documents
  • Associative retrieval of documents

 

Structured Data Analysis

 

Support for semantic analysis of structured data such as that found in relational, B.I. or flat databases is supported with following capabilities:

  • Faceted search (allowing repetitive searches to filter the results of prior searches)
  • Linked Data Analysis  (across structured and unstructured databases)
  • Data mining on Big Data collections
  • Faceted search (allowing repetitive searches to filter the results of prior searches)
  • Linked Data Analysis (across structured and unstructured databases)
  • Data Mining on Big Data collections (pattern matching and selection)
  • Predictive Analytics (locating and identifying trends in structured data)
  • Data Record Classification (naïve bayes, k-nearest neighbor)

 

asked Jan 12, 2014 by trustnetadmin (210 points)
edited Oct 12, 2015 by trustnetadmin

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