Semiautomatic Generation of

Data-Extraction Ontologies

Master’s Thesis Proposal

Yihong Ding

Querying the Web(Two Approaches)

• Enhanced query language– Examples: WebSQL, WebOQL

– Sources: structured, or restructured before parsing

• Wrapper– Enables querying in a database-like fashion

– Depends on source format • not resilient

• same topic with different formats need different wrappers

Data-Extraction Ontology

• Beyond the wrapper approach– Extraction technique for data-rich, unstructured,

multiple-record Web documents

– Does not depend on source format • resilient

• Same topic with different formats uses same ontology

• Good experimental results

Main Difficulty (Creating the Data-Extraction Ontology)

• Users must be experts– database theory

– regular expression generation

• Manual creation is impractical– Very large information sources

– Frequently added sources of interest

– Many varying text formats

Semiautomatic Data-Extraction Generation

Generation & Updating Process

Input Knowledge

Sources

GeneratedData-Extraction

Ontology

Training Document(s)

Validation Documents

Generation Process

• For this research, three steps are expected:– Gathering Knowledge

– Generating Initial Ontology

– Validation & Updating Strategy

• Ontology Generation Performance Evaluation

Example: Extract Information from Country Library Web Site (http://www.tradeport.org/ts/countries/)

Car Advertisement XML Base CIA Factbook XML Base

Learning & DiscoveringAlgorithm

All

Mileage

Make

… …

Model

Car

Capital

… …

Area

CountryName

Population

AgricultureCountry

CIA Factbook XML Base

Car Advertisement XML Base

Learning & DiscoveringAlgorithm

All

Mileage

Make

… …

Model

Car

Capital

… …

Area

CountryName

Population

AgricultureCountry

All

Mileage

Make

… …

Model

Car

Capital

… …

Area

CountryName

Population

AgricultureCountry

Performance Evaluation

• Measure precision and recall for each lexical object set in generated extraction ontology

• Measure was generated with respect to could have been

generated

• Measure was generated with respect to should not have

been generated

Delimitation

Will not …• Consider all storage formats for existing knowledge

– XML

• Consider all document formats– HTML

– Plain Text

• Let users update the input knowledge source at run-time

Contribution

• Semi-automatically generate a data-extraction ontology

• Exploit the existing knowledge

• Link existing data-extraction tools

• Create a partial library of regular expression recognizers