Generating DataExtraction Ontologies By Example

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Generating Data-Extraction Ontologies By Example

• Joe Zhou
• Data Extraction Group
• Brigham Young University

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Background

• World Wide Web contains a huge amount of useful
  information.
• Web data-extraction is necessary for querying the
  data of interest.
• Most of wrappers generate extraction patterns
  based on delimiters or HTML tags. So they are
  source-dependent.
• BYU ontology-based technique is resilient.

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Problem and Solution

• BYU Onto approach requires that ontology experts
  generate data-extraction ontologies for the
  domains of interest to ordinary users
• A principal effort of our research is to automate
  ontology- generation process as much as possible
• We developed a system OntoByE (Ontology By
  Example) to generate data-extraction ontologies
  semi-automatically

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Extraction Ontology

• Object sets, Relationship sets and Constraints
• Data frames for Lexical Object Sets

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Extraction Ontology
Object sets, Relationship sets and Constraints
Data frame for Digital Zoom
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OntoByE System Overview and Architecture
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OntoByE User Interface
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Form Editor Basic Form Elements
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Form Editor Nesting Forms
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Form Editor Creating Forms for Digital Camera
Application
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Training Web Document Preparation
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Ontology Generator Workflow
Data Frames
Object Sets, Relationship Sets and Constraints
Extraction Ontology
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Ontology Generator Form Analyzer
Sample Form
Object and Realationship Sets and Constraints

• BaseForm 01 A 1
• BaseForm 03 B 1
• BaseForm 0 C 1
• BaseForm 03 D1 1 D2 1 D3 1
• BaseForm 0 E1 1 E2 1 E3 1

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Ontology Generator Form Analyzer

• Object and Relationship Sets
• and Constraints

• Digital Camera application Forms

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Ontology Generator Context Phrase Locator
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Ontology Generator Data Frame Matcher

• Data Frame Matching Heuristics
• Number of matched data
• Data Frame Ranking Heuristics
• Number of matched data
• Keywords and/or Contexts Matching
• Order of Specialization/Generalization

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Ontology Generator Keyword and Context
Recognizer
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Ontology Generator Data Frame Editor
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Extraction Ontology
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Experimental Preparation

• Selected two domains of interest
• Digital Camera Application and Apartment Rental
  Application
• Constructed an initial data frame library
• Integer (any integer value), SmallPositiveInteger
  (from 1 to 99), SingleDigit (from 0 to 9),
  RealNumber (any real value), SmallPositiveReal
  (from 0.01 to 99.99), Date, Email, PhoneNumber,
  and Price
• Created application-dependent forms for each
  application
• Collected 5 sample pages from different web sites
  for each domain
• Marked desired data on sample pages

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Experimental Results Digital Camera Application
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Experimental Results Digital Camera Application
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Experimental Results Apartment Rental
Application
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Experimental Results Apartment Rental
Application
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Experimental Results Apartment Rental
Application
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Experimental Observations Strengths of OntoByE

• OntoByE provides a friendly and intuitive
  interface to help ordinary users describe data of
  interest without exposing them to abstract
  ontology concepts
• With a small initial data frame library and a
  small set of sample pages, OntobyE works well to
  search for and suggest appropriate existing data
  frames for object sets with application-independen
  t values
• OntoByE successfully recognizes possible keywords
  and contexts for user marked-data from sample
  pages and helps users to create new data frames
  with the keywords and contexts

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Experimental Observations Limitations of OntoByE

• The performance of searching for or constructing
  data frames by OntoByE is limited by the scope
  and the quality of prior knowledge
• The accuracy and completeness of keyword and
  context expression construction are limited by
  the number and representativeness of user samples
• Constructing value expressions for
  application-dependent data frames requires that
  users know how to write regular expressions.

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Conclusion

• We implemented a user-friendly interface for
  ordinary users to take advantage of our
  ontology-based web data-extraction approach.
• We developed a framework for interacting with
  ordinary users to generate ontologies by example.
• Our experiments demonstrate that OntoByE works
  well to generate ontologies with assistance of a
  limited prior knowledge. As time goes by, along
  with the expansion of prior knowledge, OntoByE
  will achieve better performance.

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Future Work

• Have OntoByE learn to build application-dependent
  lexicons for users applications
• Improve the sub-components of the back-end
  ontology generator, e.g. Context Phrase Locator

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• The end