Cui Tao

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Ontology Generation, Information Harvesting and
Semantic Annotation For Machine-Generated Web
Pages

• Cui Tao
• PhD Dissertation Defense

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Motivation

• Birth date of my great grandpa
• Price and mileage of red Nissans, 1990 or newer
• Protein and amino acids information of gene
  cdk-4?
• US states with property crime rates above 1

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Search by Search Engine
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Search the Hidden Web

• The Hidden Web
• Hidden behind forms
• Hard to query

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Query for Data

• The Hidden Web
• Hidden behind forms
• Hard to query

Find the protein and the animo-acids
information for gene cdk-4"
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A Web of Pages ? A Web of Knowledge

• Web of Knowledge
• Machine-understandable
• Publicly accessible
• Queriable by standard query languages
• Semantic annotation
• Domain ontologies
• Populated conceptual model
• Problems to resolve
• How do we create ontologies?
• How do we annotate pages for ontologies?

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Contributions of Dissertation Work

• Web of Pages ? Web of Knowledge
• Knowledge meta-knowledge extraction
• Reformulation as machine-understandable
  knowledge
• Automatic semi-automatic solutions via
• Sibling tables (TISP/TISP)
• User-created forms (FOCIH)

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Automatic Annotation with TISP(Table
Interpretation with Sibling Pages)

• Recognize tables (discard non-tables)
• Locate table labels
• Locate table values
• Find label/value associations

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Recognize Tables
Layout Tables (discard)
Data Table
Nested Data Tables
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Find Label/Value Associations
Example (Identification.Gene model(s).Protein,
Identification.Gene model(s).2) WPCE28918
1 2
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Interpretation TechniqueSibling Page Comparison
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Interpretation TechniqueSibling Page Comparison
Same
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Interpretation TechniqueSibling Page Comparison
Almost Same
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Interpretation TechniqueSibling Page Comparison
Different
Same
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Technique Details

• Unnest tables
• Match tables in sibling pages
• Perfect match (table for layout ? discard )
• Reasonable match (sibling table)
• Determine use table-structure pattern
• Discover pattern
• Pattern usage
• Dynamic pattern adjustment

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Table Unnesting
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Table Structure Patterns

• Regularity Expectations
• (lttrgtlt(tdth)gt L lt(tdth)gt V)n
• lttrgt(lt(tdth)gt L)n
• (lttrgt(lt(tdth)gt V)n)

Pattern combinations are also possible.
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Table Structure Patterns
lttrgt(lt(tdth)gt L)n (lttrgt(lt(tdth)gt V)n)
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Pattern Usage
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Dynamic Pattern Adjustment
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TISP

• Automatic ontology generation
• Automatic information annotation

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Ontology Generation OSM

• Object set table labels
• Lexical labels that associate with actual values
• Non-lexical labels that associate with other
  tables
• Relationship set table nesting
• Constraints updates based on observation

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Ontology Generation OWL

• Object set OWL class
• Relationship set OWL object property
• Lexical object set
• OWL data type property
• Different annotation properties to keep track of
  the provenance

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Generated Ontology
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Generated Ontology
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RDF Graph
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Query the Data
Find the protein and the animo-acids
information for gene cdk-4"
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TISP Evaluation

• Applications
• Commercial car ads
• Scientific molecular biology
• Geopolitical US states and countries
• Data gt 2,000 tables in 35 sites
• Evaluation
• Initial two sibling pages
• Correct separation of data tables from layout
  tables?
• Correct pattern recognition?
• Remaining tables in site
• Information properly extracted?
• Able to detect and adjust for pattern variations?

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Experimental Results

• Table recognition correctly discarded 157 of 158
  layout tables
• Pattern recognition correctly found 69 of 72
  structure patterns
• Extraction and adjustments 5 path adjustments
  and 34 label adjustments ? all correct

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TISP Performance

• Performance depends on TISP
• TISP test set
• Generates all ontologies correctly
• Annotates all information in tables correctly

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Form-based Ontology Creation and Information
Harvesting (FOCIH)

• Personalized ontology creation by form
• General familiarity
• Reasonable conceptual framework
• Appropriate correspondence
• Transformable to ontological descriptions
• Capable of accepting source data
• Automated ontology creation
• Automated information harvesting

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Form Creation
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Created Sample Form
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Generated Ontology View
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Source-to-Form Mapping
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Source-to-Form Mapping
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Source-to-Form Mapping
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Source-to-Form Mapping
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Almost Ready to Harvest

• Need reading path DOM-tree structure
• Need to resolve mapping problems
• Pattern recognition
• Instance recognition

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Reading Path
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Pattern Instance Recognition
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Pattern Instance Recognition
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Pattern Instance Recognition
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Pattern Instance Recognition
list pattern, delimiter is ,
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Pattern Instance Recognition
list pattern, delimiter is regular expression
for percentage numbers and a comma
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Pattern Instance Recognition
list pattern, delimiter is regular expression
for percentage numbers and a comma
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Can Now Harvest
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Can Now Harvest
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Can Now Harvest
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Semantic Annotation
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Semantic Annotation
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Semantic Annotation
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Semantic Annotation
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Semantic Annotation
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Semantic Query
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FOCIH Performance

• Ontology creation
• Semantic annotation
• Depends on TISP performance
• Depends on pattern and instance recognition
  performance

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FOCIH Performance

• Pattern and instance recognition
• Works with highly regular data
• Tested 71 mappings
• 25 full-string values (25/25 correct)
• 38 substring values (29/38 correct)
• 8 list patterns (6/8 correct)

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FOCIH Difficulties
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FOCIH Difficulties
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FOCIH Difficulties
No selection
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WoK via TISP
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WoK via TISP
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WoK via FOCIH
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WoK via FOCIH
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Contributions

• TISP automatic sibling table interpretation
• TISP
• Automatic ontology generation based on
  interpreted tables
• Automatic semantic annotation for interpreted
  tables
• FOCIH
• Semi-automatic personalized ontology creation
• Automatic personalized information harvesting and
  semantic annotation
• All together contributes to turning the current
  web of pages into a web of Knowledge

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Future Work

• Sibling pages in addition to sibling tables
• Reverse engineer from ontologies to forms as a
  basis for information harvesting for already
  defined ontologies.