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Automating Schema Matching for Data Integration
David W. Embley Brigham Young University Funded by NSF
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Information Exchange Source Target Information Extraction Schema
Leverage this … … to do this Schema Matching
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Presentation Outline Information Extraction
Schema Matching for HTML Table Direct Schema Matching Indirect Schema Matching Conclusions and Future Work
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Information Extraction
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Extracting Pertinent Information from Documents
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A Conceptual Modeling Solution
Year Price Make Mileage Model Feature PhoneNr Extension Car has is for 1..* 0..1 0..*
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Car-Ads Ontology Car [->object]; Car [0..1] has Year [1..*];
Car [0..1] has Make [1..*]; Car [0...1] has Model [1..*]; Car [0..1] has Mileage [1..*]; Car [0..*] has Feature [1..*]; Car [0..1] has Price [1..*]; PhoneNr [1..*] is for Car [0..*]; PhoneNr [0..1] has Extension [1..*]; Year matches [4] constant {extract “\d{2}”; context "([^\$\d]|^)[4-9]\d,[^\d]"; substitute "^" -> "19"; }, … End;
![Car-Ads Ontology Car [->object]; Car [0..1] has Year [1..*];](http://slideplayer.com/slide/15299943/92/images/7/Car-Ads+Ontology+Car+%5B-%3Eobject%5D%3B+Car+%5B0..1%5D+has+Year+%5B1..%2A%5D%3B.jpg "Car [0..1] has Make [1..*]; Car [0...1] has Model [1..*]; Car [0..1] has Mileage [1..*]; Car [0..*] has Feature [1..*]; Car [0..1] has Price [1..*]; PhoneNr [1..*] is for Car [0..*]; PhoneNr [0..1] has Extension [1..*]; Year matches [4] constant {extract \d{2} ; context ([^\$\d]|^)[4-9]\d,[^\d] ; substitute ^ -> 19 ; }, … End;")
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Recognition and Extraction
Car Year Make Model Mileage Price PhoneNr Subaru SW $1900 (363) Elandra (336) HONDA ACCORD EX 100K (336) Car Feature 0001 Auto 0001 AC 0002 Black door 0002 tinted windows 0002 Auto 0002 pb 0002 ps 0002 cruise 0002 am/fm 0002 cassette stero 0002 a/c 0003 Auto 0003 jade green 0003 gold
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Schema Matching for HTML Tables
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Table-Schema Matching (Basic Idea)
Many tables on the Web Ontology-Based Extraction: Works well for unstructured or semistructured data What about structured data – tables? Method: Form attribute-value pairs Do extraction Infer mappings from extraction patterns
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Problem: Different Schemas
Target Database Schema {Car, Year, Make, Model, Mileage, Price, PhoneNr}, {PhoneNr, Extension}, {Car, Feature} Different Source Table Schemas {Run #, Yr, Make, Model, Tran, Color, Dr} {Make, Model, Year, Colour, Price, Auto, Air Cond., AM/FM, CD} {Vehicle, Distance, Price, Mileage} {Year, Make, Model, Trim, Invoice/Retail, Engine, Fuel Economy} ?
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Problem: Attribute is Value
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Problem: Attribute-Value is Value
? ?
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Problem: Value is not Value
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Problem: Implied Values
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Problem: Missing Attributes
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Problem: Compound Attributes
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Problem: Merged Values
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Problem: Values not of Interest
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Problem: Factored Values
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Problem: Split Values
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Problem: Information Behind Links
Table extending over several pages Single-Column Table (formated a list)
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Solution Form attribute-value pairs (adjust if necessary)
Do extraction Infer mappings from extraction patterns
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Solution: Remove Internal Factoring
Unnest: (Model, Year, Colour, Price, Auto, Air Cond, AM/FM, CD)*(Year, Colour, Price, Auto, Air Cond, AM/FM, CD)*Table Legend ACURA Discover Nesting: Make, (Model, (Year, Colour, Price, Auto, Air Cond, AM/FM, CD)*)*
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Solution: Replace Boolean Values
Yes, AutoAir CondAM/FM AM/FM Air Cond. Auto CD Table Yes, CD ACURA ACURA Legend
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Solution: Form Attribute-Value Pairs
AM/FM Air Cond. Auto CD ACURA ACURA Legend <Make, Honda>, <Model, Civic EX>, <Year, 1995>, <Colour, White>, <Price, $6300>, <Auto, Auto>, <Air Cond., Air Cond.>, <AM/FM, AM/FM>
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Solution: Adjust Attribute-Value Pairs
AM/FM Air Cond. Auto CD ACURA ACURA Legend <Make, Honda>, <Model, Civic EX>, <Year, 1995>, <Colour, White>, <Price, $6300>, <Auto>, <Air Cond>, <AM/FM>
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Solution: Do Extraction
AM/FM Air Cond. Auto CD ACURA ACURA Legend <Make, Honda>, <Model, Civic EX>, <Year, 1995>, <Colour, White>, <Price, $6300>, <Auto>, <Air Cond>, <AM/FM>
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Solution: Infer Mappings
Make(Model, Year, Colour, Price, Auto, Air Cond, AM/FM, CD)*(Year, Colour, Price, Auto, Air Cond, AM/FM, CD)*Table YearTable Note: Mappings produce sets for attributes. Joining to form records is trivial because we have OIDs for table rows (e.g. for each Car). Model(Year, Colour, Price, Auto, Air Cond, AM/FM, CD)*Table AM/FM Air Cond. Auto CD Each row is a car. ACURA ACURA Legend {Car, Year, Make, Model, Mileage, Price, PhoneNr}, {PhoneNr, Extension}, {Car, Feature}
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Solution: Do Extraction
Model(Year, Colour, Price, Auto, Air Cond, AM/FM, CD)*Table AM/FM Air Cond. Auto CD ACURA ACURA Legend {Car, Year, Make, Model, Mileage, Price, PhoneNr}, {PhoneNr, Extension}, {Car, Feature}
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Solution: Do Extraction
AM/FM Air Cond. Auto CD ACURA ACURA Legend PriceTable {Car, Year, Make, Model, Mileage, Price, PhoneNr}, {PhoneNr, Extension}, {Car, Feature}
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Solution: Do Extraction
AM/FM Air Cond. Auto CD ACURA ACURA Legend ColourFeatureColourTable AutoFeatureAutoAutoTable Air Cond.FeatureAir Cond. Air Cond.Table AM/FMFeatureAM/FMAM/FMTable CDFeatureCDCDTable Yes, Yes, Yes, Yes, {Car, Year, Make, Model, Mileage, Price, PhoneNr}, {PhoneNr, Extension}, {Car, Feature}
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Experiment Tables from 60 sites 10 “training” tables 50 test tables
357 mappings (from all 60 sites) 172 direct mappings (same attribute and meaning) 185 indirect mappings (29 attribute synonyms, 5 “Yes/No” columns, 68 unions over columns for Feature, 19 factored values, and 89 columns of merged values that needed to be split)
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Results 10 “training” tables 50 test tables 16 missed mappings
100% of the 57 mappings (no false mappings) 94.6% of the values in linked pages (5.4% false declarations) 50 test tables 94.7% of the 300 mappings (no false mappings) On the bases of sampling 3,000 values in linked pages, we obtained 97% recall and 86% precision 16 missed mappings 4 partial (not all unions included) 6 non-U.S. car-ads (unrecognized makes and models) 2 U.S. unrecognized makes and models 3 prices (missing $ or found MSRP instead) 1 mileage (mileages less than 1,000)
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Direct Schema Matching
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Attribute Matching for Populated Schemas
Central Idea: Exploit All Data & Metadata Matching Possibilities (Facets) Attribute Names Data-Value Characteristics Expected Data Values Data-Dictionary Information Structural Properties
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Approach Target Schema T Source Schema S Framework
Individual Facet Matching Combining Facets Best-First Match Iteration
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Example Target Schema T Source Schema S Year Model Make Car Mileage
Miles Year has 0:1 Year has 0:1 Make has 0:1 Feature has 0:* Make has 0:1 Model has 0:1 Car Cost has 0:1 Model has 0:1 Car Style Phone has 0:1 0:1 has 0:* 0:1 0:1 has has has Mileage Miles Cost Target Schema T Source Schema S
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Individual Facet Matching
Attribute Names Data-Value Characteristics Expected Data Values
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Attribute Names Target and Source Attributes WordNet
S : B WordNet C4.5 Decision Tree: feature selection, trained on schemas in DB books f0: same word f1: synonym f2: sum of distances to a common hypernym root f3: number of different common hypernym roots f4: sum of the number of senses of A and B
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WordNet Rule The number of different common hypernym roots of A and B
The sum of distances of A and B to a common hypernym The sum of the number of senses of A and B
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Confidence Measures
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Data-Value Characteristics
C4.5 Decision Tree Features Numeric data (Mean, variation, standard deviation, …) Alphanumeric data (String length, numeric ratio, space ratio)
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Confidence Measures
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Expected Data Values Target Schema T and Source Schema S
Regular expression recognizer for attribute A in T Data instances for attribute B in S Hit Ratio = N’/N for (A, B) match N’ : number of B data instances recognized by the regular expressions of A N: number of B data instances
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Confidence Measures
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Combined Measures 1 1 1 1 1 Threshold: 0.5
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Final Confidence Measures
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Experimental Results This schema, plus 6 other schemas Matched: 100%
32 matched attributes 376 unmatched attributes Matched: 100% Unmatched: 99.5% “Feature” ---”Color” “Feature” ---”Body Type” F1 93.8% F2 84% F3 92% 98.9% 97.9% 98.4% F1: WordNet F2: Value Characteristics F3: Expected Values
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Indirect Schema Matching
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Schema Matching Target Source Color Year Year Make Feature Make &
Model Body Type Target Car Cost Model Car Style Phone Mileage Miles Cost Source
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Mapping Generation Direct Matches as described earlier:
Attribute Names based on WordNet Value Characteristics based on value lengths, averages, … Expected Values based on regular-expression recognizers Indirect Matches: Direct matches Structure Evaluation Union Selection Decomposition Composition
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Union and Selection Target Source Color Year Year Make Feature Make &
Model Body Type Target Car Cost Model Car Style Phone Mileage Miles Cost Source
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Decomposition and Composition
Color Year Year Make Feature Make & Model Body Type Target Car Cost Model Car Style Phone Mileage Miles Cost Source
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Example Taken From [MBR, VLDB’01]
Structure Example Taken From [MBR, VLDB’01] PO PurchaseOrder Items POShipTo POBillTo POLines InvoiceTo DeliverTo Count Address ItemCount Item City Street City Street Item ItemNumber City Street Line Qty UoM Quantity UnitOfMeasure Target Source
![Example Taken From [MBR, VLDB’01]](http://slideplayer.com/slide/15299943/92/images/55/Example+Taken+From+%5BMBR%2C+VLDB%E2%80%9901%5D.jpg "Structure. Example Taken From [MBR, VLDB’01] PO. PurchaseOrder. Items. POShipTo. POBillTo. POLines. InvoiceTo. DeliverTo. Count. Address. ItemCount. Item. City. Street. City. Street. Item. ItemNumber. City. Street. Line. Qty. UoM. Quantity. UnitOfMeasure. Target. Source.")
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Structure (Nonlexical Matches)
PO PurchaseOrder Items POShipTo POBillTo POLines InvoiceTo DeliverTo DeliverTo Count Address Count Item City Street City Street Item ItemNumber City Street Line Qty UoM Quantity UnitOfMeasure Target Source
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Structure (Join over FD Relationship Sets, …)
PO PurchaseOrder Items POShipTo POBillTo POLines InvoiceTo DeliverTo City Count City Count Item Street City Street City Street Item Street ItemNumber Line Qty UoM Quantity UnitOfMeasure Target Source
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Structure (Lexical Matches)
PO PurchaseOrder Items POShipTo POBillTo POLines InvoiceTo DeliverTo City City Count Count City City Count Count Item Street Street City City Street Street City City Street Street Item Street Street ItemNumber Line Line Qty Qty UoM Quantity Quantity UnitOfMeasure Target Source
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Experiments Methodology Measures Precision Recall F Measure
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Results Indirect Matches: 94% (precision, recall, F-measure) 98 93 96
Applications (Number of Schemes) Precision (%) Recall F Correct False Positive Negative Course Schedule (5) 98 93 96 119 2 9 Faculty Member (5) 100 140 Real Estate (5) 92 94 235 20 10 Indirect Matches: 94% (precision, recall, F-measure) Data borrowed from Univ. of Washington [DDH, SIGMOD01] Rough Comparison with U of W Results (Direct Matches only) * Course Schedule – Accuracy: ~71% * Faculty Members – Accuracy, ~92% * Real Estate (2 tests) – Accuracy: ~75%
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Conclusions and Future Work
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Conclusions Table Mappings Direct Attribute Matching
Tables: 94.7% (Recall); 100% (Precision) Linked Text: ~97% (Recall); ~86% (Precision) Direct Attribute Matching Matched 32 of 32: 100% Recall 2 False Positives: 94% Precision Direct and Indirect Attribute Matching Matched 494 of 513: 96% Recall 22 False Positives: 96% Precision
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Current & Future Work: Improve and Extend Indirect Matching
Improve Object-Set Matching (e.g. Lex/non-Lex) Add Relationship-Set Matching Computations
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Current & Future Work: Tables Behind Forms
Crawling the Hidden Web Filling in Forms from Global Queries
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Current & Future Work: Developing Extraction Ontologies
Creation from Knowledge Sources and Sample Application Pages K Ontology + Data Frames, Lexicons, … RDF Ontologies User Creation by Example
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Current & Future Work: and Much More …
Table Understanding Microfilm Census Records Generate Ontologies by Reading Tables …
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