1. Filtering Multiple-Record Web Documents Based on Application Ontologies Presenter: L. Xu Advisor: D.W.Embley

2. Examples D1: CarD2: Item for Sale or Rent

3. Car Ontology Car[->object]; Car[0..0.975..1] has Year; Car[0..0.925..1] has Make; Car[0..0.908..1] has Model; Car[0..0.45..1] has Mileage; Car[0..2.1..*] has Feature; Car[0..0.8..1] has Price; PhoneNr is for Car[1..1.15..*]; Year matches [4] constant {extract “\d{2}”; context "([^\$\d]|^)[4-9]\d,[^\d]"; substitute "^" -> "19"; },. End;

4. Filtering Heuristics H1: Density H2: Expected-values H3: Grouping

5. H1: Density Car Total Number of Characters: 2048 Number of Matched Characters: 626 Density: 0.306 Item for Rent or Sale Total Number of Characters: 196 Number of Matched Characters: 2671 Density: 0.073

6. H2: Expected-values OV D1D2 Year 0.98 16 6 Make 0.93 10 0 Model 0.91 12 0 Mileage 0.45 6 2 Price 0.80 11 8 Feature 2.10 29 0 PhoneNr 1.15 1511 D1: 0.996 D2: 0.567 ov D1 D2

7. H3: Grouping Year: 2000 Year: 1989 Make: Subaru Model: SW------ Nr of Distinct "One Max" Object:3 Price: 1900 Year: 1998 Model: Elantra Year: 1994------ Nr of Distinct "One Max" Object:3. Grouping Factor is: 0.865 Year: 1999 Year: 1998 Year: 1960 Mileage: 10000 Nr of Distinct "One Max" Object:2 Mileage: 401000 Year: 1940 Price: 17500 Year: 10971 Nr of Distinct "One Max" Object: 3. Grouping Factor is: 0.5

8. Combining Heuristics Decision tree learning algorithm C4.5 –Learning task: suitability –Performance measure: accuracy –Training experience: human classified documents Training set –20 positive examples (from 10 geographical regions of US States) –30 negative examples Test set –10 positive examples –20 negative examples

9. Generated Rules Car application –H2 <= 0.8767:NO –H2 > 0.8767:YES Obituary application –H2 <= 0.6793:NO –H2 > 0.6793 –| H1 <= 0.2171:NO –| H1 > 0.2171:YES Universal rule –H3 <= 0.625 –| H1 <= 0.369: NO –| H1 > 0.369 –| | H2 <= 0.6263: NO –| | H2 > 0.6263: YES –H3 > 0.625: YES

10. Experiment Results Car application –accuracy96.7% –precision100% –recall91% Obituary application –accuracy96.7% –precision91% –recall100% Universal rule –accuracy93.4% –precision84% –recall100%

11. False Drop Example

12. False Positive Example

13. Summary Objective : Automatically filter multiple-record web documents. Approach: Filtering heuristics –Density –Expected-values –Grouping Result : ~95% accuracy