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
Wrapper-Driven Data Extraction

• Web data extraction
• Obtain user-specified information from Web
  documents
• Wrapper
• Convert implicit HTML data into explicit
  formatted data
• Data-source-specified, high performance
• Examples
• SoftMealy, STALKER, WIEN, Omini, ROADRUNNER,

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Common Problem of Wrappers
SoftMealy

• ltLIgt ltA HREF""gt Mani Chandy lt/Agt,
• ltIgtProfessor of Computer Sciencelt/Igt
• and ltIgtExecutive Officer for Computer
• Sciencelt/Igt

Resiliency fixed domain changeable layout
Scalability unchanged existing wrapper extendable
domain and functions
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Data-Extraction Ontology

• Structure
• Object sets
• Relationship sets
• Participation constraints
• Data frames
• Pros resilient and scalable
• Cons hard to create
• Knowledge requirements
• Tedious and error-prone work

• Car -gt object
• Car 01 has Make 1
• Make matches 10
• constant extract "\baudi\b"
• end
• Car 01 has Model 1
• Model matches 25
• constant extract "80"
• context "\baudi\S\s80\b"
  
• end
• Car 01 has Mileage 1
• Mileage matches 8
• constant extract "\b1-9\d0,2k"
• substitute "kK" -gt "000"
• end

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Motif of Ontology Generation
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Thesis Statement

• Given knowledge base
• Input sample Web pages of interest
• Output a data-extraction ontology for the domain
  of interest
• Between input and output this is the work of
  this thesis

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Ontology-Generation Procedure
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Primary Knowledge Source

• Requirements
• Available
• General in coverage
• Rich in meaningful relationship
• Encoded in or easily converted to XML
• Mikrokosmos (?K) Ontology
• Developed by NMSU jointly with U.S. DoD
• Contains over 5000 concepts
• Connects to an average 14 links per concept
• Represented in XML format

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Integrated Knowledge Base
KNOWLEDGE BASE
?K Ontology
Data-Frame Library
Lexicons
Synonym Dictionary (WordNet)
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Ontology-Generation Procedure
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Domain Specification

• Training documents
• Data-rich
• Narrow in topic breadth
• Preprocessing

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Example Car Advertisement
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 13,695 221-1250 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
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Ontology-Generation Procedure
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Concept Selection

• Selection strategies
• Compare a string with the name of a concept
• Compare a string with the values belonging to a
  concept
• Apply data-frame recognizers to recognize a
  string

KB
ltPHONE-NRgt
00 Buick Century Stk HU7159 Green 9,319,
714-2200To Apply By Phone, 1-877-228-9486, OREM
Utah
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Concept Selection

• Reasons of conflict
• Synonymy
• Polysemy
• Conflict resolution
• Same-string only one meaning
• Favor longer over shorter
• Context decides meaning

KB
02 Buick Century Custom, Pwr Seat, Nada Retail
13,695 221-1250.
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Ontology-Generation Procedure
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Relationship Retrieval
KB
ltAUTOMOBILEgt
ltMILEAGEgt
ltYEARgt
ltPRICEgt
ltPHONE-NRgt
ltAUDIO-MEDIA-ARTIFACTgt
ltCENTURYgt
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Ontology-Generation Procedure
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Constraint Discovery
02 Buick Century, lo mi, mint cond, green, pwr
seat, 11,999. 373-4445 dlr 2755
AUTOMOBILE 01 IsA.ARTIFACT.CostofProduction
PRICE 11
00 Buick Century Stk HU7159 Green 9,319,
714-2200To Apply By Phone, 1-877-228-9486, OREM
Utah
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Ontology-Generation Procedure
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Ontology Generation

• concept nodes ? object sets
• paths ? relationship sets
• discovered constraints ? participation
  constraints
• concept recognizers ? data frames

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Automatically Generated Ontology -- Car
Advertisement
(01) Automobile -gt object (02) Automobile
01 has Mileage 11 (03) Automobile 01
IsA.ARTIFACT.CostOfProduction Price 11 (12)
Price 11 IsA.SCALARATTRIBUTE.MeasuredIn.MEASUR
INGUNIT.Subclasses Year 0 (20) Automobile
01 relatesTo PhoneNr 1 relatesTo
ArtifactPart 1 relatesTo Mileage 1
relatesTo Truck 1 relatesTo
AudioMediaArtifact 1 relatesTo
CommunicationDevice 1 relatesTo ControlEvent
1 relatesTo TravelEvent 1
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Ontology-Generation Procedure
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Updating Strategies

• Remove all bad relationship sets
• Modify remaining incorrect relationship sets
• Substitute incorrect object sets
• Reduce long n-ary relationship sets
• Fix participation constraints
• Adjust names or re-arrange sequences
• Add new relationship sets

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Final Ontology

• Car -gt object
• Car 01 has Year 1
• Car 01 has Mileage 1
• Car 01 has Price 1
• PhoneNr 1 is for Car 01
• PhoneNr 01 has Extension 1
• Car 0 has Feature 1
• Car 01 has Make 1
• Car 01 has Model 1

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Evaluation Criteria

• Basic measures
• POG (Precision of Ontology Generation)
• ROG (Recall of Ontology Generation)
• Human constraints
• PROG (Pseudo-ROG)
• Comparing with an expert-created ontology
• Knowledge base constraints
• EPROG (Effective-PROG)
• Correctness dependency
• DEPROG (Dependent-EPROG)
• For example relationship sets depends on object
  sets

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Evaluation Results
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Discussion of Results

• Bottleneck cannot generate what not in the
  knowledge base
• Object sets
• Concept-selection procedure works well
• Desired concept not shown in training records
• Rarely occurring concept ? not severe even if we
  dont fix the error
• Example extension
• Aggregation and union
• USAddressCity, USAddressState, USAddressZipCode ?
  Location
• CropPlant, AnimalProduct, FruitFoodStuff ?
  AgriculturalProduct
• Close-meaning concepts FurniturePart ? Furnished

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Discussion of Results

• Relationship sets
• Binary relationship sets over 95
• Most errors due to incorrectly generated object
  sets
• Semantically incorrect relationship sets
• Price IsA.SCALARATTRIBUTE.MeasuredIn.MEASURINGUNIT
  .Subclasses Year
• n-ary relationship sets (usually huge)
• Participation constraints
• Error due to lack of training examples
• How much is enough?

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Knowledge Base Extensibility

• Add SALT -- a new knowledge source
• Successfully integrated into existing KB
• Sample new relationship set (DOE abstract domain)
• CrudeOil IsA.PHYSICALOBJECT.Location.PLACE.Subclas
  ses Nation

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Conclusion

• Experimented with knowledge-base construction and
  extension
• Standardized application domain specification
• Generated data-extraction ontologies from a
  specified domain and an integrated knowledge base
• Showed DEPROG results of more than 70 on average
  and over 90 for well-defined domains

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Future Work

• Build a general-purpose knowledge source for
  data-extraction usage
• Study more about data frames
• Can a system correctly identify concepts with
  data frames?
• Can a system update a data frame to fit a special
  situation?
• Can a system generate a data frame from a
  collection of information of interest?