Transcript TISP++ - Tango

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
“cdk-4"
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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)
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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) = WP:CE28918
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Interpretation Technique:
Sibling Page Comparison
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Interpretation Technique:
Sibling Page Comparison
Same
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Interpretation Technique:
Sibling Page Comparison
Almost Same
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Interpretation Technique:
Sibling 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:
• (<tr><(td|th)> {L} <(td|th)> {V})n
• <tr>(<(td|th)> {L})n
(<tr>(<(td|th)> {V})n)+
•…
Pattern combinations are
also possible.
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Table Structure Patterns
<tr>(<(td|th)> {L})n
(<tr>(<(td|th)> {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
Generated Ontology
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: > 2,000 tables in 35 sites
• Evaluation
– Initial two sibling pages
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•
Correct separation of data tables from layout tables?
Correct pattern recognition?
– Remaining tables in site
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•
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
regular expression
for decimal number
left
context
right
context
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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:
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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.
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