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Automating Schema Matching for Data Integration

Presentation outline on information extraction and schema matching for structured data integration, with solutions for matching different schemas, extracting pertinent information from documents, and inferring mappings.

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Automating Schema Matching for Data Integration

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  1. Automating Schema Matchingfor Data Integration David W. Embley Brigham Young University Funded by NSF

  2. Leverage this … … to do this Information Exchange Source Target Information Extraction Schema Matching

  3. Presentation Outline • Information Extraction • Schema Matching for HTML Table • Direct Schema Matching • Indirect Schema Matching • Conclusions and Future Work

  4. Information Extraction

  5. Extracting Pertinent Information from Documents

  6. Year Price 1..* 1..* 1..* has has Make 1..* Mileage 0..1 0..1 0..1 0..1 has has Car 0..1 0..1 0..* is for PhoneNr has has 1..* Model 0..1 1..* 1..* has Feature 1..* Extension A Conceptual Modeling Solution

  7. 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;

  8. Car Feature 0001 Auto 0001 AC 0002 Black 0002 4 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 Car Year Make Model Mileage Price PhoneNr 0001 1989 Subaru SW $1900 (363)835-8597 0002 1998 Elandra (336)526-5444 0003 1994 HONDA ACCORD EX 100K (336)526-1081 Recognition and Extraction

  9. Schema Matching forHTML Tables

  10. 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

  11. ? 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}

  12. Problem: Attribute is Value

  13. ? ? Problem: Attribute-Value is Value

  14. Problem: Value is not Value

  15. Problem: Implied Values

  16. Problem: Missing Attributes

  17. Problem: Compound Attributes

  18. Problem: Merged Values

  19. Problem: Values not of Interest

  20. Problem: Factored Values

  21. Problem: Split Values

  22. Table extending over several pages Single-Column Table (formated a list) Problem: Information Behind Links

  23. Solution • Form attribute-value pairs (adjust if necessary) • Do extraction • Infer mappings from extraction patterns

  24. ACURA ACURA Legend Unnest: (Model, Year, Colour, Price, Auto, Air Cond, AM/FM, CD)*(Year, Colour, Price, Auto, Air Cond, AM/FM, CD)*Table Solution: Remove Internal Factoring Discover Nesting: Make, (Model, (Year, Colour, Price, Auto, Air Cond, AM/FM, CD)*)*

  25. Auto Air Cond. AM/FM CD Auto AM/FM Auto Air Cond. AM/FM CD AM/FM Air Cond. AM/FM Auto Air Cond. AM/FM CDTable AutoAir CondAM/FM Yes, Yes, Yes, Yes, Solution: Replace Boolean Values ACURA ACURA Legend

  26. Auto Air Cond. AM/FM CD Auto AM/FM Auto Air Cond. AM/FM CD AM/FM Air Cond. AM/FM Auto Air Cond. AM/FM Solution: Form Attribute-Value Pairs ACURA ACURA Legend <Make, Honda>, <Model, Civic EX>, <Year, 1995>, <Colour, White>, <Price, $6300>, <Auto, Auto>, <Air Cond., Air Cond.>, <AM/FM, AM/FM>

  27. Auto Air Cond. AM/FM CD Auto AM/FM Auto Air Cond. AM/FM CD AM/FM Air Cond. AM/FM Auto Air Cond. AM/FM Solution: Adjust Attribute-Value Pairs ACURA ACURA Legend <Make, Honda>, <Model, Civic EX>, <Year, 1995>, <Colour, White>, <Price, $6300>, <Auto>, <Air Cond>, <AM/FM>

  28. Auto Air Cond. AM/FM CD Auto AM/FM Auto Air Cond. AM/FM CD AM/FM Air Cond. AM/FM Auto Air Cond. AM/FM Solution: Do Extraction ACURA ACURA Legend <Make, Honda>, <Model, Civic EX>, <Year, 1995>, <Colour, White>, <Price, $6300>, <Auto>, <Air Cond>, <AM/FM>

  29. Auto Air Cond. AM/FM CD Auto AM/FM Auto Air Cond. AM/FM CD AM/FM Air Cond. AM/FM Auto Air Cond. AM/FM Make(Model, Year, Colour, Price, Auto, Air Cond, AM/FM, CD)*(Year, Colour, Price, Auto, Air Cond, AM/FM, CD)*Table Each row is a car. YearTable Model(Year, Colour, Price, Auto, Air Cond, AM/FM, CD)*Table Note: Mappings produce sets for attributes. Joining to form records is trivial because we have OIDs for table rows (e.g. for each Car). Solution: Infer Mappings ACURA ACURA Legend {Car, Year, Make, Model, Mileage, Price, PhoneNr}, {PhoneNr, Extension}, {Car, Feature}

  30. Auto Air Cond. AM/FM CD Auto AM/FM Auto Air Cond. AM/FM CD AM/FM Air Cond. AM/FM Auto Air Cond. AM/FM Model(Year, Colour, Price, Auto, Air Cond, AM/FM, CD)*Table Solution: Do Extraction ACURA ACURA Legend {Car, Year, Make, Model, Mileage, Price, PhoneNr}, {PhoneNr, Extension}, {Car, Feature}

  31. Auto Air Cond. AM/FM CD Auto AM/FM Auto Air Cond. AM/FM CD AM/FM Air Cond. AM/FM Auto Air Cond. AM/FM Solution: Do Extraction ACURA ACURA Legend PriceTable {Car, Year, Make, Model, Mileage, Price, PhoneNr}, {PhoneNr, Extension}, {Car, Feature}

  32. Auto Air Cond. AM/FM CD Auto AM/FM Auto Air Cond. AM/FM CD AM/FM Air Cond. AM/FM Auto Air Cond. AM/FM Solution: Do Extraction ACURA ACURA Legend ColourFeatureColourTable  AutoFeatureAutoAutoTable  Air Cond.FeatureAir Cond. Air Cond.Table  AM/FMFeatureAM/FMAM/FMTable  CDFeatureCDCDTable Yes, Yes, Yes, Yes, {Car, Year, Make, Model, Mileage, Price, PhoneNr}, {PhoneNr, Extension}, {Car, Feature}

  33. 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)

  34. Results • 10 “training” tables • 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)

  35. Direct Schema Matching

  36. Attribute Matchingfor Populated Schemas • Central Idea: Exploit All Data & Metadata • Matching Possibilities (Facets) • Attribute Names • Data-Value Characteristics • Expected Data Values • Data-Dictionary Information • Structural Properties

  37. Approach • Target Schema T • Source Schema S • Framework • Individual Facet Matching • Combining Facets • Best-First Match Iteration

  38. Year Year Year Year Make Make Make Feature Make has has has has has 0:1 0:1 0:1 0:1 0:* 0:1 0:1 Car Cost Model Model Model Car Model has has 0:1 has 0:1 has Phone Mileage Miles Example Car Car Style 0:1 has 0:* 0:1 0:1 has has has Mileage Miles Cost Target Schema T Source Schema S

  39. Individual Facet Matching • Attribute Names • Data-Value Characteristics • Expected Data Values

  40. Attribute Names • Target and Source Attributes • T : A • 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

  41. The number of different common hypernym roots of A and B The sum of the number of senses of A and B The sum of distances of A and B to a common hypernym WordNet Rule

  42. Confidence Measures

  43. Data-Value Characteristics • C4.5 Decision Tree • Features • Numeric data (Mean, variation, standard deviation, …) • Alphanumeric data (String length, numeric ratio, space ratio)

  44. Confidence Measures

  45. 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

  46. Confidence Measures

  47. 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Combined Measures Threshold: 0.5

  48. Final Confidence Measures 0 0 0

  49. F1 93.8% F2 84% F3 92% F1 98.9% F2 97.9% F3 98.4% F1: WordNet F2: Value Characteristics F3: Expected Values Experimental Results • This schema, plus 6 other schemas • 32 matched attributes • 376 unmatched attributes • Matched: 100% • Unmatched: 99.5% • “Feature” ---”Color” • “Feature” ---”Body Type”

  50. Indirect Schema Matching

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