Data Frames Version 3 Proposal

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Data Frames Version 3 Proposal
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Data Frames Version 2

• Year matches 2
• constant extract "\d2"
• context "(\\d)\d2,\dkK"
  0.5,
• extract "\d2"
• context "(\\d)\d2,\d"
  0.6,
• extract "\d2"
• context "\b'\d2\b" 0.8
• end
• Mileage matches 8
• constant extract "\b1-9\d1,2k" 0.6,
• extract "1-9\d?,\d3" 0.3
• keyword "\bmiles\b", "\bmi\.", "\bmi\b"
• end
• Also except, substitute, filter phrases lexicons

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Kimballs Ontology Editor
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Internal Representation

• Replace SQL field length with arbitrary type
  field
• This is the internal representation
• Type is either lexical or nonlexical
• Type could be the name of an object set in the
  ontology
• Or it could be the name of a type in whatever
  language will be used to implement methods (more
  on this later), together with a units name (e.g.
  miles, meters, grams, pounds)

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Methods

• Add a method phrase to data frames
• Conceptually they are restricted derived object
  sets and relationship sets
• We only declare method signatures in data frames
• Another language (e.g. Java) is used to define
  the method body
• Our tool will generate a template in which the
  programmer can write method bodies
• The template will have OO structures that allow
  read-only access to the seamless model/data
  instance
• Keyword phrases may also apply to methods

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Canonicalization Methods

• Each value phrase may have an associated
  canonicalization method
• The purpose is to convert the extracted value
  string into a common form
• The data frame may have a default
  canonicalization method that applies if there is
  no individual method for a value phrase

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Inheritance

• Inheritance is defined more cleanly
• Generalization/specialization will indicate
  inheritance hierarchy
• The internal representation cannot be overridden
  in specializations
• Multiple parents must have the same internal
  representation
• Individual inherited phrases can be deleted or
  overridden
• New phrases can be added
• In the case of name conflict, we require fully
  qualified names to be used (no automatic
  disambiguation)

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General Constraints

• We may decide to implement a limited form of
  general constraint in the ontology
• E.g. Birth Date lt Death Date
• Or Event Distance.toMiles() lt 26
• If so, we may want to implement operator
  overloading (something like C)
• The general constraint issue is not core to the
  current data frame discussion, but it has
  interesting ramifications

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Other Issues

• How to integrate methods and confidence values
  into record-assembly heuristics
• Ontos system will have to be rewritten
• Extract into model instance, not SQL tables
• We can always generate database tables later if
  wed like
• Ontologies created graphically and stored as XML