Information Extraction and Ontology Learning Guided by Web Directory

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Information Extraction and Ontology Learning
Guided by Web Directory

• Authors Martin Kavalec
• Vojtech Svátek
• Presenter Mark Vickers

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Outline

• Introduction
• Mining Indicator Terms
• Integrating Rainbow
• Ontological Analysis of Web Directories
• IE and Ontology Learning
• Future Work
• Related Work
• Assessment

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Introduction

• Goal
• to extract information about (mostly generic)
  products, services and areas of competence of
  companies, from the free text chunks embedded in
  web presentations.
• Taking advantage of
• Collections of extraction patterns
• Ontologies of problem domains
• Approach Combine Information Extraction With
  Ontologies
• Ontologies can improve quality of IE
• Extracted information can improve/extend
  ontologies
• Bootstrapping

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Introduction

• Uses Open Directory (http//dmoz.org)
• Obtain labeled training data
• Lightweight ontologies

The Open Directory Project is the largest, most
comprehensive human-edited directory of the Web.
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Mining Indicator Terms

• Informative terms generic names of products
• Indicator terms situated near informative terms
• Example our assortment includes
• in our shop you can buy
• Assumption Directory headings coincide with
  informatives
• Purpose Generate extraction patterns based on
  Indicator terms
• They use deeper linguistic techniques

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Mining Indicator Terms

• Example
• /Manufacturing/Materials/Metals/Steel/

Informative terms

• Match headings with text pages to find sentences
  containing informative terms
• Grab nearby words as indicator terms
• Generate extraction patterns from indicator terms

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Mining Indicator Terms

• Choosing Indicator Terms
• Syntactical analysis Link Grammar Parser
• Chose verbs occurring closest in parse tree to
  informative word
• Arrange verbs into a frequency table
• Order by ratio of frequency near informative term
  to frequency in general
• Chose 8 most promising verbs

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Mining Indicator Terms

• Preliminary Testing
• Sampled 14,500 sentences containing heading terms
  
• Randomly chose 130 sentences with indicators
• Manually labeled to estimate if informative term
  was present or not
• Example
• We are equipped to run any grade of corrugated
  from E-flute to Triplewall, including all
  government grades.

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Mining Indicator Terms

• Preliminary Test Results

Coverage
Non-Filtered 10 20
Pre-Filtered 70 80
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Integration into Rainbow

• RAINBOW
• (Reusable Architecture for INtelligent Brokering
  Of Web information access)
• Web Analysis Tasks
• Sentence Extraction
• Explicit Metadata
• HTML Structure
• Inline Image
• Link Topology Structure
• Page Similarity
• Internal Communication based on SOAP
• Will use ontologies for verifying semantic
  consistency of web services provided within the
  distributed system

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Integration into Rainbow

• Rainbow will help solve coverage problem of
  directory links pointing to barren pages
• Using Analysis of
• Keywords and HTML Structure on start-up pages
• URLs of embedded links
• Metadata Extractor will be navigated towards
  promising pages.
• Looking for about-us or profile to find more
  syntactically correct text, for example.

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Ontological Analysis of Web Directories
-Industries - Construction_and_Maintenance -
Materials_and_supplies - Masonry_and_Stone
- Natural_Stone - International_Sources
- Mexico

• Terms and Phrases in single heading belong to a
  small set of classes
• Parent-child relations belong to particular
  classes corresponding to deep ontological
  relations.

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Ontological Analysis of Web Directories
Class-subclass Relations
Class
Named Relations
Reflexive Binary Relations

• Meta-ontology of directory headings

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Ontological Analysis of Web Directories

• Interpretation Rules

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IE and Ontology Learning

• Extracting with plain indicator terms with simple
  heuristics works
• But Even Better
• Learn indicators for each class
• Use ontology analysis to classify indicators
  found
• Fill in database templates true IE

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IE and Ontology Learning
Closed Loop Strategy
Learn class-specific indicators
Classify Headings
Human Classifies Directory Headings (WordNet)
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Future Work

• Complete the Information extraction ontology
  learning loop.
• With relation to Semantic Web, they want to adapt
  technique to the standards of usual explicit
  metadata
• Example The information extracted can be forged
  to RDF triples, with indicator collections
  accessible over the web

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Related Work

• Combining IE and Ontologies (without use of web
  directories)
• Bootstrapping an Ontology-Based Information
  Extraction Systems
• Advantages of using Link Grammar Parser
• Learning to Generate Semantic Annotation for
  Domain Specific Sentences
• Using Yahoo to classify whole documents
• Turning Yahoo into an Automatic Web-Page
  Classifier
• Similar work aimed at more structured information
  using search engines
• Extracting Patterns and Relations form the World
  Wide Web
• Bootstrapping and other statistical methods for
  IE
• Text Classification by Bootstrapping with
  Keywords
• Learning Dictionaries of Information Extraction
  by Multi-Level Bootstrapping

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Assessment

• I dont think indicator term learning is done
  (even though they say it is)
• Counts on not yet decided Ontology learning
  techniques
• Need to develop an official directory