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playence prose: Textual Semantic Search Engine

playence prose is a cutting-edge, semantic-based, search and browsing engine specifically designed to handle very large amounts of textual corporate content. playence prose is a highly configurable best-of-need solution, offering all features and characteristics required by large and medium organizations.

 

Screenshots

 

Features

  • Automatic multi-language knowledge extraction:
    • Locate and extract the relevant domain knowledge present.
    • Annotate assets efficiently for later use.
  • Configurable domain knowledge:
    • Customization of domain model and knowledge to use.
    • Different views on the same asset depending on user needs.
  • Semantic search:
    • Keywords, entities and conceptual relations search.
    • Query expansion and reduction.
    • Synonym support and subsution among others.
    • Predictive search.
  • Faceted search:
    • Creation of structured properties from unstructured content.
    • Search and filtering over defined properties.
  • Named Entity Recognition (NER) and Topic Recognition:
    • Identification of names of persons, locations, dates, brands, etc.
    • Identification of main topic and annotation.
  • Multiformat support:
    • Word processing documents, eMail, HTML, XML. 
  • Multilingualism:
    • Support for a wide variety of languages.
    • Complex cross-language query and asset retrieval.
  • Dynamic document interlinking:
    • Dynamically association creation among documents based on Linked Data cloud.
    • Relationships among asset in heterogeneous information sources, internal or external.
  • Browsing and asset navigation:
    • Powerful browsing engine to keep sight of huge amounts of textual information.
    • Eases finding, discovering and accessing the required textual resources.
  • Relationship viewer:
    • Snapshot view of a portion of the subjacent data model.
    • Document set filtering by way of query expansion and reduction.
  • Probabilistic tag clouds:
    • Snapshot of the most relevant tags to the current set of documents.
    • Document set filtering by way of query expansion and reduction.

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