On Embedding MachineProcessable Semantics into Documents

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On Embedding Machine-Processable Semantics into
Documents

• Krishnaprasad Thirunarayan
• Department of Computer Science Engineering
• Wright State University
• Dayton, OH-45435, USA

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Talk Outline

• Background and Motivation (Why?)
• Goals (What?)
• Details (How?)
• Conclusions

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Background and Motivation
4

Content Extraction Formalize doc, using
controlled vocabulary
Heterogeneous Doc.
Spec. Defn. Rep.
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Problems with this approach to content extraction

• Archiving spec (for human comprehension)
  separately from its formalization is not
  conducive traceability.
• Manual extraction from spec (from scratch) for
  each use is labor intensive, time consuming, and
  prone to typographical errors.

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Observation

• Conceptually, every piece of information in an
  extraction owes its existence to a phrase in
  spec, and possibly, controlled vocabulary.
• So, explore techniques to maintain correspondence
  between a spec fragment and its formalization.

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Goal
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General Problem

• Embed domain-specific mark-up (annotations) into
  human sensible document
• to make explicit semantics of content text and
  complex data, and
• to augment an interpretation in a modular
  fashion.
• Document text Human comprehensible
• Semantic Mark-up Machine processable

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Details (How?)
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Nature of Specs

• Semi-structured
• Heterogeneous
• Text
• Tables
• Images
• Constrained technical vocabulary
• Available as MS Word document

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Pre-processing Spec

• Abstract content from spec document by removing
  display oriented information
• Save text
• Save tabular data, preserving grid layout
• Retain links to images
• Note Save As text option in MS Word inadequate

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Heterogeneous Document
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XML generated by Majix
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ASCII Output
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Annotating Pre-processed Spec

• Embedding Machine Processable Semantics
• Recognizing and tagging text using controlled
  vocabulary
• By product of Document Indexing and Semantic
  Search
• Tagging tabular data to make explicit its
  semantics Same grid layout, but different
  interpretation and dependencies based on
  headings
• Explore XML-based programming language Water for
  defining data and its behavior (semantics)

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Locating Controlled Vocabulary Terms
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Example Table
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Example of Tagged Table

• Thickness (mm) Tensile Strength (ksi) Yield
  Strength (ksi)
• table.ltsetHeading thickness strength.tensile
  strength.yield/gt
• 0.50 and under 165
  155
• table.ltaddRow 0 0.50 165
  155 /gt
• 0.50 - 1.00 160
  150
• table.ltaddRow 0.50 1.00 160
  150 /gt
• 1.00 - 1.50 155
  145
• table.ltaddRow 1.00 1.50 155
  145 /gt ...

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Example of Processing Code

• ltdefclass table rowsrequiredvector
  headingoptionalvectorgt
• ltdefmethod setHeading trequired tsrequired
  ysrequiredgt
• ltset headingltvector t ts ys/gt/gt
• lt/gt
• ltdefmethod addRow smin smax ts ysgt
• ltset rows
• table.rows.ltinsert ltvector smin
  smax ts ys/gt/gt/gt
• lt/gt
• ltdefmethod computeYieldStrengthgt
  lt/gt
• ltdefmethod computeTensileStrengthgt lt/gt
• lt/gt

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(contd)

• ltdefclass table rowsrequiredvector
  headingoptionalvectorgt
• ltdefmethod computeTensileStrengthgt
• ltset tempfluid.Thickness/gt
• ltset i0/gt
• ltdogt
• ltuntil ltand temp.ltless table.rows.ltget
  i/gt.1/gt
• temp.ltmore_or_equal
  table.rows.ltget i/gt.0/gt /gt gt
• table.rows.ltget i/gt.2
• lt/untilgt
• ltset ii.ltplus 1/gt/gt
• lt/dogt
• lt/gt
• lt/gt

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(contd)

• ltdefclass table rowsrequiredvector
  headingoptionalvectorgt
• lt/gt
• fluid.ltset Thickness0.60gt
• lttry
• ltset TensileStrengthtable.ltcomputeTensileStre
  ngth/gt/gt
• TensileStrength
• gt
• "TABLE out of range error occurred"
• lt/trygt

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Water

• XML-based OO Scripting Language
• Facilitates creating Web Services
• Run methods remotely via web-browser
• Generalizes dynamic typing to constraint checking
• Conformance of actuals to formals

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Pros and cons

• Encoding Improvement
• Amount of tagging can be controlled by suitably
  delimiting table data and annotating it with
  corresponding string-processing method
• Master Copy Update
• Changes to spec requires manual modification to
  archived annotated version.
• Irregular Tables in Specs
• Different units, etc

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Some Related Work

• Microsoft Smart Tags
• Recognize controlled words in Office 2003
  documents and associate predefined list of
  actions with each occurrence
• SHOE
• Table data in a declarative (logic) language

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Prolog rendition

• strengthTableRow( 0, 0.50, 165, 155).
• strengthTableRow(0.50, 1.00, 160, 150).
• strengthTableRow(1.00, 1.50, 155, 145).
• ...
• strengthTable(Thickness, TensileStrength,
  YieldStrength) -
• strengthTableRow(L, U,
  TensileStrength, YieldStrength),
• L lt Thickness, U gt Thickness.
• thicknessToTensileStrength(Thickness,
  TensileStrength) -
• strengthTable(Thickness,
  TensileStrength, _).
• thicknessToYieldStrength(Thickness,
  YieldStrength) -
• strengthTable(Thickness, _,
  YieldStrength).
• ?- thicknessToYieldStrength(0.6,YS).

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Conclusions
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A Step towards Holy Grail

• Ultimately enable authoring and/or extracting,
  human-comprehensible and machine-processable
  parts of a document hand in hand, and keep them
  side by side.