Semiautomatic Generation of Resilient DataExtraction Ontologies

1
Semiautomatic Generation of Resilient
Data-Extraction Ontologies

• Yihong Ding
• Data Extraction Group
• Brigham Young University
• Sponsored by NSF

2
Introduction

• Wrapper-driven data extraction
• Pros data-source-specified, high performance
• Cons lack of resiliency and scalability
• Ontology-driven data extraction
• Pros application-domain-specified, resilient and
  scalable
• Cons hard to create
• Objective
• Generating data-extraction ontologies

3
Generation Architecture
pre-processing
interact if necessary
Concept Selection
Extraction Processing
Relation Retrieval
Constraint Discovery
Integrated Knowledge Base
Results Storage
Result Evaluation
pre-processing
Knowledge Sources
4
Knowledge Base Construction

• Knowledge Sources
• Mikrokosmos (?K) Ontology
• Data-Frame Library
• Additional Lexicons
• WordNet
• Integration of Knowledge Base

5
Application Specification
Record 1 00 GrandAM SE, Sunfire Red, CD, AC, PW,
PLGreat Condition, 10,800, Call 798-3446
Record 2 02 Buick Century Custom, Pwr Seat,
Nada Retail 13,695Only 12,695. 221-1250 Record
3 02 Buick Century, lo mi, mint cond, 11,999.
373-4445 dlr 2755 Record 4 00 Buick Century
Stk HU7159 Green 9,319, 714-2200To Apply By
Phone, 1-877-228-9486, OREM Utah
6
Domain Allocation concept selection

• Select concepts using string-matching with object
  values
• Resolve conflict by context or semantic meanings

7
Domain Allocation relationship retrieval

• Find paths among selected concept nodes
• Retrieve cluster representing application domain

Record 1 00 GrandAM SE, Sunfire Red, CD, AC, PW,
PLGreat Condition, 10,800, Call 798-3446
Record 2 02 Buick Century Custom, Pwr Seat,
Nada Retail 13,695Only 12,695. 221-1250 Record
3 02 Buick Century, lo mi, mint cond, 11,999.
373-4445 dlr 2755 Record 4 00 Buick Century
Stk HU7159 Green 9,319, 714-2200To Apply By
Phone, 1-877-228-9486, OREM Utah
8
Domain Allocation constraint discovery

• Discover participation times for each object
  values
• Specify discovered values to be participation
  constraints

02 Buick Century, lo mi, mint cond, green, pwr
seat, 11,999. 373-4445 dlr 2755
AUTOMOBILE 01 has MAKE 1
AUTOMOBILE 0 has FEATURE 1
00 Buick Century Stk HU7159 Green 9,319,
714-2200To Apply By Phone, 1-877-228-9486, OREM
Utah
AUTOMOBILE 01 has PRICE 11
9
Ontology Generation

• Initial ontology automatically generated
• Updated ontology user tuning
• Expectation
• Rejecting existence much easier than adding new
• Modification as less as possible

10
Evaluation and Results

• Evaluation
• Compare Generated vs. Expert-created
• POG (Precision of Ontology Generation)
• PROG (Pseudo-Recall of Ontology Generation)
• EPROG (Effective-PROG)
• Results
• Three testing domains Apt-Rental, Used-Auto-Ads,
  Nation-Essence
• Average POG less than 0.23
• Lowest EPROG is around 0.70, highest is almost
  1.0

11
Conclusion

• Exploits existing knowledge
• Specifies application domain
• Allocates domain inside the knowledge base
• Generates a data-extraction ontology
• Shows effective recall of more than 70 on
  average