Discovering Direct and Indirect Matches for Schema Elements Li Xu Data Extraction Group Brigham Young University Sponsored by NSF

Car Problem Source Car Year Cost Style Year Feature Cost Car Phone Target Car Miles Mileage Model Make & Model Color Body Type

Applications Data Integration Schema Integration Message Mapping Data Translation

Approach Direct Matches Indirect Matches Union Selection Composition Decomposition

Union and Selection Car Source Car Year Cost Style Year Feature Cost Car Phone Target Car Miles Mileage Model Make & Model Color Body Type

Composition and Decomposition Car Source Car Year Cost Style Year Feature Cost Car Phone Target Car Miles Mileage Model Make & Model Color Body Type

Matching Techniques Terminological Relationships Value Characteristics Expected Data Values Structure

Terminological Relationships WordNet Machine-Learned Rules Example: (Make, Brand) The number of different common hypernym roots of A and B Sum of distances of A and B to a common hypernym The sum of the number of senses of A and B

Value Characteristics Machine Learning Features [LC94] String length, numeric ratio, space ratio. Mean, variation, coefficient variation, standard deviation;

Make & ModelBrand Model Expected Values Application Concepts Data Recognizers CarMake  “ford”  “honda”  … CarModel  “accord”  “mustang”  “taurus”  … Ford Mustang Ford Taurus Ford F150 … CarMake. CarModel Legend Mustang A4 … CarModel CarMake TargetSource Acura Audi BMW …

Structure PO POShipToPOBillToPOLines CityStreetCityStreetItem Count LineQtyUoM PurchaseOrder DeliverToInvoiceTo Items ItemItemCount ItemNumber QuantityUnitOfMeasure CityStreet Address TargetSource

Structure (Cont.) PO POShipToPOBillToPOLines CityStreetCityStreetItem Count LineQtyUoM PurchaseOrder DeliverToInvoiceTo Items ItemCount ItemNumber QuantityUnitOfMeasure CityStreet Address DeliverTo TargetSource

Structure (Cont.) PO POBillToPOLines CityStreetCityStreetItem Count LineQtyUoM PurchaseOrder InvoiceTo Items ItemCount ItemNumber QuantityUnitOfMeasure City Street City Street POShipToDeliverTo TargetSource

Structure (Cont.) PO POBillToPOLines CityStreetCityStreetItem Count PurchaseOrder InvoiceTo Items ItemCount City Street City Street LineQtyUoM ItemNumber Quantity LineQtyUoM ItemNumber Quantity LineQty QuantityUnitOfMeasure POShipToDeliverTo TargetSource

Structure (Cont.) PO POBillToPOLines CityStreetCityStreetItem Count LineQtyUoM PurchaseOrder InvoiceTo Items ItemCount ItemNumber Quantity City Street City Street City Street City Street Count LineQty QuantityUnitOfMeasure POShipToDeliverTo TargetSource

Experiments Methodology Measures Precision Recall F Measure

Results Applications (Number of Schemes) Precision (%) Recall (%) F (%) CorrectFalse Positive False Negative Course Schedule (5) Faculty Member (5) Real Estate (5) Data borrowed from Univ. of Washington Indirect Matches: 94% (precision, recall, F-measure)

Ground-Truthing Contact Cell phone Office phone Firm location Fax Firm name Agent name Agent’s Or Firm’s

Limitation (Expected Data Value)

Contributions Direct Matches Indirect Matches Expected values Structure High Precision and High Recall