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Cui TaoPhD Dissertation Defense

Ontology Generation, Information Harvesting and Semantic Annotation For Machine-

Generated Web Pages

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MotivationBirth 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?

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

Data Table

Layout Tables (discard)

NestedData Tables

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Find Label/Value AssociationsExample:(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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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.

Table Structure Patterns

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<tr>(<(td|th)> {L})n

(<tr>(<(td|th)> {V})n)+

Table Structure Patterns

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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

Generated Ontology

Generated Ontology

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RDF Graph

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Query the DataFind 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• 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 Recognitionregular expression

for decimal numberleft 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:– 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.