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Presenting Semantic Ontology Information Using Natural Language

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  • david_dodds_2001
    (Copyright 2009 David Dodds) Semantic information located in ontologies can be wrapped in (English), or other) natural language statements by building an
    Message 1 of 1 , Jun 16, 2009
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      (Copyright 2009 David Dodds)
      Semantic information located in ontologies can be
      wrapped in (English), or other) natural language
      statements by building an instance graph network
      of the instances in the system's ontologies where
      items match or depict the semantic processing
      undertaken by the system. As we have seen in
      past articles elements / items in an SVG graphics
      file may have ontological items associated with
      them. Also, MathML items may have ontological
      items associated with them. One way to indicate
      (for a given moment) which ontological items are
      relevant (in depicting the semantics of the data set
      under examination, such as the SVG bargraph or
      an MathML function) is to display the ontological
      terms / items as their RDF URIs. This might be
      done with the aid of a network graph. While
      accurate such a depiction is ugly and unhelpful to
      the more casual user, one who is not versed in
      W3C RDF experience. This means most people.

      A more useful way to depict the semantic content
      of material, such as the bargraph or a MathML
      equation is to depict the information in a way
      which is like the communications used every
      day by non-experts (in the semantic web domain)
      and this means natural language constructions.
      It is implied, therefore, that when possible,
      special usage symbols and terms are replaced
      by verbiage and that of more likely common usage.

      This leads us to the point that we are then faced
      with explaining to the computer (via programming)
      how to decide what to say. Some will view this as
      the inverse problem faced by programmers
      constructing natural language understanding systems,
      a far greater complexity than building a fancy
      parser for grammatical categorizing (parsing).

      One of the ways that we can approach the
      programming for making natural language-like
      output of semantic content is to restrict the
      complexity of the modes of expression (fluidity)
      to be less sophisticated than a human natural
      language user would produce if he were outputting
      similar semantic information using natural language,
      such as English.
      In the next part of this article we see how
      language semantic templates are used to perform
      this processing. We also see how the TaleSpin
      process contributes to this activity.
      (Discussed are : How are Conceptual
      Dependency Theory , and Frames, of value to
      this pursuit? How do we decide what to say next?)
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