What Do You Want Semantic Understanding Youve Got

What Do You Want— Semantic Understanding? (You’ve Got to be Kidding) David W. Embley Brigham Young University Funded in part by the National Science Foundation 1

Presentation Outline § § § § Grand Challenge Meaning, Knowledge, Information, Data Fun and Games with Data Information Extraction Ontologies Applications Limitations and Pragmatics Summary and Challenges 2

Grand Challenge Semantic Understanding Can we quantify & specify the nature of this grand challenge? 3

Grand Challenge Semantic Understanding “If ever there were a technology that could generate trillions of dollars in savings worldwide …, it would be the technology that makes business information systems interoperable. ” (Jeffrey T. Pollock, VP of Technology Strategy, Modulant Solutions) 4

Grand Challenge Semantic Understanding “The Semantic Web: … content that is meaningful to computers [and that] will unleash a revolution of new possibilities … Properly designed, the Semantic Web can assist the evolution of human knowledge …” (Tim Berners-Lee, …, Weaving the Web) 5

Grand Challenge Semantic Understanding “ 20 th Century: Data Processing “ 21 st Century: Data Exchange “The issue now is mutual understanding. ” (Stefano Spaccapietra, Editor in Chief, Journal on Data Semantics) 6

Grand Challenge Semantic Understanding “The Grand Challenge [of semantic understanding] has become mission critical. Current solutions … won’t scale. Businesses need economic growth dependent on the web working and scaling (cost: $1 trillion/year). ” (Michael Brodie, Chief Scientist, Verizon Communications) 7

Why Semantic Understanding? § Because we’re overwhelmed with data We succeed in managing information • Point and click too slow if we can “[take] data and [analyze] it • “Give and me what I want when want it. ” [simplify] it and [tell]I people thekey information they want, progress § Becauseexactly it’s the to revolutionary rather than all the information they • Automated and knowledge sharing couldinteroperability have. ” - Jimin. Gray, Microsoft Research • Automated negotiation e-business • Large-scale, in-silico experiments in e-science 8

What is Semantic Understanding? Semantics: “The meaning or the interpretation of a word, sentence, or other language form. ” Understanding: “To grasp or comprehend [what’s] intended or expressed. ’’ - Dictionary. com 9

Can We Achieve Semantic Understanding? “A computer doesn’t truly ‘understand’ anything. ” … But computers can manipulate terms “in ways that are useful and meaningful to the human user. ” - Tim Berners-Lee Key Point: it only has to be good enough. And that’s our challenge and our opportunity! 10

Presentation Outline § § § § Grand Challenge Meaning, Knowledge, Information, Data Fun and Games with Data Information Extraction Ontologies Applications Limitations and Pragmatics Summary and Challenges 11

Information Value Chain Meaning Knowledge Information Data Translating data into meaning 12

Foundational Definitions § Meaning: knowledge that is relevant or activates § Knowledge: information with a degree of certainty or community agreement § Information: data in a conceptual framework § Data: attribute-value pairs - Adapted from [Meadow 92] 13

Foundational Definitions § Meaning: knowledge that is relevant or activates § Knowledge: information with a degree of certainty or community agreement (ontology) § Information: data in a conceptual framework § Data: attribute-value pairs - Adapted from [Meadow 92] 14

Foundational Definitions § Meaning: knowledge that is relevant or activates § Knowledge: information with a degree of certainty or community agreement (ontology) § Information: data in a conceptual framework § Data: attribute-value pairs - Adapted from [Meadow 92] 15

Foundational Definitions § Meaning: knowledge that is relevant or activates § Knowledge: information with a degree of certainty or community agreement (ontology) § Information: data in a conceptual framework § Data: attribute-value pairs - Adapted from [Meadow 92] 16

Data § Attribute-Value Pairs • Fundamental for information • Thus, fundamental for knowledge & meaning 17

Data § Attribute-Value Pairs • Fundamental for information • Thus, fundamental for knowledge & meaning § Data Frame • Extensive knowledge about a data item Everyday data: currency, dates, time, weights & measures Textual appearance, units, context, operators, I/O conversion • Abstract data type with an extended framework 18

Presentation Outline § § § § Grand Challenge Meaning, Knowledge, Information, Data Fun and Games with Data Information Extraction Ontologies Applications Limitations and Pragmatics Summary and Challenges 19

? Olympus C-750 Ultra Zoom Sensor Resolution: Optical Zoom: Digital Zoom: Installed Memory: Lens Aperture: Focal Length min: Focal Length max: 4. 2 megapixels 10 x 4 x 16 MB F/8 -2. 8/3. 7 6. 3 mm 63. 0 mm 20

? Olympus C-750 Ultra Zoom Sensor Resolution: Optical Zoom: Digital Zoom: Installed Memory: Lens Aperture: Focal Length min: Focal Length max: 4. 2 megapixels 10 x 4 x 16 MB F/8 -2. 8/3. 7 6. 3 mm 63. 0 mm 21

? Olympus C-750 Ultra Zoom Sensor Resolution: Optical Zoom: Digital Zoom: Installed Memory: Lens Aperture: Focal Length min: Focal Length max: 4. 2 megapixels 10 x 4 x 16 MB F/8 -2. 8/3. 7 6. 3 mm 63. 0 mm 22

? Olympus C-750 Ultra Zoom Sensor Resolution Optical Zoom Digital Zoom Installed Memory Lens Aperture Focal Length min Focal Length max 4. 2 megapixels 10 x 4 x 16 MB F/8 -2. 8/3. 7 6. 3 mm 63. 0 mm 23

Digital Camera Olympus C-750 Ultra Zoom Sensor Resolution: Optical Zoom: Digital Zoom: Installed Memory: Lens Aperture: Focal Length min: Focal Length max: 4. 2 megapixels 10 x 4 x 16 MB F/8 -2. 8/3. 7 6. 3 mm 63. 0 mm 24

? Year Make Model Mileage Features Price Phone 2002 Ford Thunderbird 5, 500 miles Red ABS 6 CD changer keyless entry $33, 000 (916) 972 -9117 25

? Year Make Model Mileage Features Price Phone 2002 Ford Thunderbird 5, 500 miles Red ABS 6 CD changer keyless entry $33, 000 (916) 972 -9117 26

? Year Make Model Mileage Features Price Phone 2002 Ford Thunderbird 5, 500 miles Red ABS 6 CD changer keyless entry $33, 000 (916) 972 -9117 27

? Year Make Model Mileage Features Price Phone 2002 Ford Thunderbird 5, 500 miles Red ABS 6 CD changer keyless entry $33, 000 (916) 972 -9117 28

Car Advertisement Year Make Model Mileage Features Price Phone 2002 Ford Thunderbird 5, 500 miles Red ABS 6 CD changer keyless entry $33, 000 (916) 972 -9117 29

? Flight # Class From Delta 16 Coach JFK Delta 119 Coach CDG Time/Date To Time/Date 6: 05 pm 02 01 04 CDG 7: 35 am 03 01 04 0 10: 20 am 09 01 04 JFK 0 1: 00 pm 09 01 04 Stops 30

? Flight # Class From Delta 16 Coach JFK Delta 119 Coach CDG Time/Date To Time/Date 6: 05 pm 02 01 04 CDG 7: 35 am 03 01 04 0 10: 20 am 09 01 04 JFK 0 1: 00 pm 09 01 04 Stops 31

Airline Itinerary Flight # Class From Delta 16 Coach JFK Delta 119 Coach CDG Time/Date To Time/Date 6: 05 pm 02 01 04 CDG 7: 35 am 03 01 04 0 10: 20 am 09 01 04 JFK 0 1: 00 pm 09 01 04 Stops 32

? Monday, October 13, 2003 Group A USA Sweden North Korea Nigeria W 3 2 1 0 L 0 1 2 3 T 0 0 GF 11 5 3 0 GA 1 3 4 11 Pts. 9 6 3 0 Group B Brazil … W 2 L 0 T 1 GF 8 GA 2 Pts. 7 33

? Monday, October 13, 2003 Group A USA Sweden North Korea Nigeria W 3 2 1 0 L 0 1 2 3 T 0 0 GF 11 5 3 0 GA 1 3 4 11 Pts. 9 6 3 0 Group B Brazil … W 2 L 0 T 1 GF 8 GA 2 Pts. 7 34

World Cup Soccer Monday, October 13, 2003 Group A USA Sweden North Korea Nigeria W 3 2 1 0 L 0 1 2 3 T 0 0 GF 11 5 3 0 GA 1 3 4 11 Pts. 9 6 3 0 Group B Brazil … W 2 L 0 T 1 GF 8 GA 2 Pts. 7 35

? Calories Distance Time Incline Speed Heart Rate 250 cal 2. 50 miles 23. 35 minutes 1. 5 degrees 5. 2 mph 125 bpm 36

? Calories Distance Time Incline Speed Heart Rate 250 cal 2. 50 miles 23. 35 minutes 1. 5 degrees 5. 2 mph 125 bpm 37

? Calories Distance Time Incline Speed Heart Rate 250 cal 2. 50 miles 23. 35 minutes 1. 5 degrees 5. 2 mph 125 bpm 38

Treadmill Workout Calories Distance Time Incline Speed Heart Rate 250 cal 2. 50 miles 23. 35 minutes 1. 5 degrees 5. 2 mph 125 bpm 39

? Place County State Type Elevation USGS Quad Latitude Longitude Bonnie Lake Duchesne Utah Lake 10, 000 feet Mirror Lake 40. 711ºN 110. 876ºW 40

? Place County State Type Elevation USGS Quad Latitude Longitude Bonnie Lake Duchesne Utah Lake 10, 000 feet Mirror Lake 40. 711ºN 110. 876ºW 41

? Place County State Type Elevation USGS Quad Latitude Longitude Bonnie Lake Duchesne Utah Lake 10, 000 feet Mirror Lake 40. 711ºN 110. 876ºW 42

Maps Place County State Type Elevation USGS Quad Latitude Longitude Bonnie Lake Duchesne Utah Lake 10, 100 feet Mirror Lake 40. 711ºN 110. 876ºW 43

Presentation Outline § § § § Grand Challenge Meaning, Knowledge, Information, Data Fun and Games with Data Information Extraction Ontologies Applications Limitations and Pragmatics Summary and Challenges 44

Information Extraction Ontologies Source Target Information Extraction Information Exchange 45

What is an Extraction Ontology? § Augmented Conceptual-Model Instance • Object & relationship sets • Constraints • Data frame value recognizers § Robust Wrapper (Ontology-Based Wrapper) • Extracts information • Works even when site changes or when new sites come on-line 46

Extraction Ontology: Example Car [-> object]; Car [0: 1] has Year [1: *]; Car [0: 1] has Make [1: *]; … Car [0: *] has Feature [1: *]; Phone. Nr [1: *] is for Car [0: 1]; Year matches [4] constant {extract “d{2}”; context “b’[4 -9]db”; …} … Mileage matches [8] keyword {bmilesb”, “bmib. ”, …} … … 47

Extraction Ontologies: An Example of Semantic Understanding § “Intelligent” Symbol Manipulation § Gives the “Illusion of Understanding” § Obtains Meaningful and Useful Results 48

Presentation Outline § § § § Grand Challenge Meaning, Knowledge, Information, Data Fun and Games with Data Information Extraction Ontologies Applications Limitations and Pragmatics Summary and Challenges 49

A Variety of Applications § § § Information Extraction High-Precision Classification Schema Mapping Semantic Web Creation Agent Communication Ontology Generation 50

Application #1 Information Extraction 51

Constant/Keyword Recognition '97 CHEVY Cavalier, Red, 5 spd, only 7, 000 miles. Previous owner heart broken! Asking only $11, 995. #1415. JERRY SEINER MIDVALE, 566 -3800 or 566 -3888 Descriptor/String/Position(start/end) Year|97|2|3 Make|CHEV|5|8 Make|CHEVY|5|9 Model|Cavalier|11|18 Feature|Red|21|23 Feature|5 spd|26|30 Mileage|7, 000|38|42 KEYWORD(Mileage)|miles|44|48 Price|11, 995|100|105 Mileage|11, 995|100|105 Phone. Nr|566 -3800|136|143 Phone. Nr|566 -3888|148|155 52

Heuristics § § § Keyword proximity Subsumed and overlapping constants Functional relationships Nonfunctional relationships First occurrence without constraint violation 53

Keyword Proximity D=2 Year|97|2|3 Make|CHEV|5|8 Make|CHEVY|5|9 Model|Cavalier|11|18 Feature|Red|21|23 Feature|5 spd|26|30 Mileage|7, 000|38|42 KEYWORD(Mileage)|miles|44|48 Price|11, 995|100|105 Mileage|11, 995|100|105 Phone. Nr|566 -3800|136|143 Phone. Nr|566 -3888|148|155 D = 52 '97 CHEVY Cavalier, Red, 5 spd, only 7, 000 miles on her. Previous owner heart broken! Asking only $11, 995. #1415. JERRY SEINER MIDVALE, 566 -3800 or 566 -3888 54

Subsumed/Overlapping Constants Year|97|2|3 Make|CHEV|5|8 Make|CHEVY|5|9 Model|Cavalier|11|18 Feature|Red|21|23 Feature|5 spd|26|30 Mileage|7, 000|38|42 KEYWORD(Mileage)|miles|44|48 Price|11, 995|100|105 Mileage|11, 995|100|105 Phone. Nr|566 -3800|136|143 Phone. Nr|566 -3888|148|155 '97 CHEVY Cavalier, Red, 5 spd, only 7, 000 miles. Previous owner heart broken! Asking only $11, 995. #1415. JERRY SEINER MIDVALE, 566 -3800 or 566 -3888 55

Functional Relationships Year|97|2|3 Make|CHEV|5|8 Make|CHEVY|5|9 Model|Cavalier|11|18 Feature|Red|21|23 Feature|5 spd|26|30 Mileage|7, 000|38|42 KEYWORD(Mileage)|miles|44|48 Price|11, 995|100|105 Mileage|11, 995|100|105 Phone. Nr|566 -3800|136|143 Phone. Nr|566 -3888|148|155 '97 CHEVY Cavalier, Red, 5 spd, only 7, 000 miles on her. Previous owner heart broken! Asking only $11, 995. #1415. JERRY SEINER MIDVALE, 566 -3800 or 566 -3888 56

Nonfunctional Relationships Year|97|2|3 Make|CHEV|5|8 Make|CHEVY|5|9 Model|Cavalier|11|18 Feature|Red|21|23 Feature|5 spd|26|30 Mileage|7, 000|38|42 KEYWORD(Mileage)|miles|44|48 Price|11, 995|100|105 Mileage|11, 995|100|105 Phone. Nr|566 -3800|136|143 Phone. Nr|566 -3888|148|155 '97 CHEVY Cavalier, Red, 5 spd, only 7, 000 miles on her. Previous owner heart broken! Asking only $11, 995. #1415. JERRY SEINER MIDVALE, 566 -3800 or 566 -3888 57

First Occurrence without Constraint Violation Year|97|2|3 Make|CHEV|5|8 Make|CHEVY|5|9 Model|Cavalier|11|18 Feature|Red|21|23 Feature|5 spd|26|30 Mileage|7, 000|38|42 KEYWORD(Mileage)|miles|44|48 Price|11, 995|100|105 Mileage|11, 995|100|105 Phone. Nr|566 -3800|136|143 Phone. Nr|566 -3888|148|155 '97 CHEVY Cavalier, Red, 5 spd, only 7, 000 miles on her. Previous owner heart broken! Asking only $11, 995. #1415. JERRY SEINER MIDVALE, 566 -3800 or 566 -3888 58

Database-Instance Generator Year|97|2|3 Make|CHEV|5|8 Make|CHEVY|5|9 Model|Cavalier|11|18 Feature|Red|21|23 Feature|5 spd|26|30 Mileage|7, 000|38|42 KEYWORD(Mileage)|miles|44|48 Price|11, 995|100|105 Mileage|11, 995|100|105 Phone. Nr|566 -3800|136|143 Phone. Nr|566 -3888|148|155 insert into Car values(1001, “ 97”, “CHEVY”, “Cavalier”, “ 7, 000”, “ 11, 995”, “ 556 -3800”) insert into Car. Feature values(1001, “Red”) insert into Car. Feature values(1001, “ 5 spd”) 59

Application #2 High-Precision Classification 60

An Extraction Ontology Solution 61

Density Heuristic Document 1: Car Ads Document 2: Items for Sale or Rent 62

Expected Values Heuristic Document 1: Car Ads Document 2: Items for Sale or Rent Year: 3 Make: 2 Model: 3 Mileage: 1 Price: 1 Feature: 15 Phone. Nr: 3 Year: 1 Make: 0 Model: 0 Mileage: 1 Price: 0 Feature: 0 Phone. Nr: 4 63

Vector Space of Expected Values D 1 OV ______ Year 0. 98 Make 0. 93 Model 0. 91 Mileage 0. 45 Price 0. 80 Feature 2. 10 Phone. Nr 1. 15 D 1: 0. 996 D 2: 0. 567 D 1 16 10 12 6 11 29 15 D 2 6 0 0 2 8 0 11 ov D 2 64

Grouping Heuristic Document 1: Car Ads Document 2: Items for Sale or Rent { { Year Mileage … Mileage Year Price … { { { Year Make Model Price Year Model Year Make Model Mileage … 65

Car Ads --------Year Make Model ------- 3 Price Year Model Year --------3 Make Model Mileage Year --------4 Model Mileage Price Year --------4 … Grouping: 0. 875 Grouping Expected Number in Group = floor(∑ Ave 1 -Max ) = 4 (for our example) Sum of Distinct 1 -Max Object Sets in each Group Number of Groups * Expected Number in a Group 3+3+4+4 = 0. 875 4*4 2+3+2+1 = 0. 500 4*4 Sale Items --------Year Mileage ------- 2 Mileage Year Price --------3 Year Price Year --------2 Price --------1 … Grouping: 0. 500 66

Application #3 Schema Mapping 67

Problem: Different Schemas Target Database Schema {Car, Year, Make, Model, Mileage, Price, Phone. Nr}, {Phone. Nr, 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} 68

Solution: Remove Internal Factoring ACURA Legend Discover Nesting: Make, (Model, (Year, Colour, Price, Auto, Air Cond, AM/FM, CD)*)* Unnest: μ(Model, Year, Colour, Price, Auto, Air Cond, AM/FM, CD)* μ (Year, Colour, Price, Auto, Air Cond, AM/FM, CD)*Table 69

Solution: Replace Boolean Values Auto Air Cond. AM/FM CD ACURA Legend Auto AM/FM Auto Air Cond. AM/FM CD AM/FM Air Cond. AM/FM AutoβAir CondβAM/FM β CD Table βYes, 70

Solution: Form Attribute-Value Pairs Auto Air Cond. AM/FM CD ACURA Legend Auto AM/FM Auto Air Cond. AM/FM CD AM/FM Air Cond. AM/FM Auto Air Cond. AM/FM <Make, Honda>, <Model, Civic EX>, <Year, 1995>, <Colour, White>, <Price, $6300>, <Auto, Auto>, <Air Cond. , Air Cond. >, <AM/FM, AM/FM>, <CD, > 71

Solution: Adjust Attribute-Value Pairs Auto Air Cond. AM/FM CD ACURA Legend Auto AM/FM Auto Air Cond. AM/FM CD AM/FM Air Cond. AM/FM Auto Air Cond. AM/FM <Make, Honda>, <Model, Civic EX>, <Year, 1995>, <Colour, White>, <Price, $6300>, <Auto>, <Air Cond>, <AM/FM> 72

Solution: Do Extraction Auto Air Cond. AM/FM CD ACURA Legend Auto AM/FM Auto Air Cond. AM/FM CD AM/FM Air Cond. AM/FM Auto Air Cond. AM/FM 73

Solution: Infer Mappings Auto Air Cond. AM/FM CD ACURA Legend Auto AM/FM Auto Air Cond. AM/FM CD AM/FM Air Cond. AM/FM Auto Air Cond. AM/FM μ(Year, EachπMake rowμis(Model, car. πModel μ(Year, Table Year, Colour, Price, Auto, Colour, Air Cond, AM/FM, Colour, Price, Auto, Air Cond, AM/FM, CD)*Table πa. Year Table Price, Auto, CD)* Air Cond, AM/FM, CD)* {Car, Year, Make, Model, Mileage, Price, Phone. Nr}, {Phone. Nr, Extension}, {Car, Feature} Note: Mappings produce sets for attributes. Joining to form records is trivial because we have OIDs for table rows (e. g. for each Car). 74

Solution: Infer Mappings Auto Air Cond. AM/FM CD ACURA Legend Auto AM/FM Auto Air Cond. AM/FM CD AM/FM Air Cond. AM/FM Auto Air Cond. AM/FM πModelμ(Year, Colour, Price, Auto, Air Cond, AM/FM, CD)*Table {Car, Year, Make, Model, Mileage, Price, Phone. Nr}, {Phone. Nr, Extension}, {Car, Feature} 75

Solution: Do Extraction Auto Air Cond. AM/FM CD ACURA Legend Auto AM/FM Auto Air Cond. AM/FM CD AM/FM Air Cond. AM/FM Auto Air Cond. AM/FM πPrice. Table {Car, Year, Make, Model, Mileage, Price, Phone. Nr}, {Phone. Nr, Extension}, {Car, Feature} 76

Solution: Do Extraction Auto Air Cond. AM/FM CD ACURA Legend Auto AM/FM Auto Air Cond. AM/FM CD AM/FM Air Cond. AM/FM Auto Air Cond. AM/FM ρ Colour←Feature π Colour. Table U ρ Auto←Feature π Auto β Auto. Table U ρ Air Cond. ←Feature π Air Cond. Yes, Air Cond. Table U ρ AM/FM CDTable β Yes, AM/FM←Feature π AM/FM β Yes, Table U ρ CD←Featureπ CDβ Yes, {Car, Year, Make, Model, Mileage, Price, Phone. Nr}, {Phone. Nr, Extension}, {Car, Feature} 77

Application #4 Semantic Web Creation 78

The Semantic Web § Make web content accessible to machines § What prevents this from working? • Lack of content • Lack of tools to create useful content • Difficulty of converting the web to the Semantic Web 79

Converting Web to Semantic Web 80

Superimposed Information 81

Application #5 Agent Communication 82

Agents must: The Problem 1 - share ontologies, 2 - speak the same language, 3 - pre-agree on message format. Requiring these assumptions precludes agents from interoperating on the fly “The holy grail of semantic integration in architectures” is to “allow two agents to generate needed mappings between them on the fly without a priori agreement and without them having built-in knowledge of any common ontology. ” [Uschold 02] 83

Solution • Eliminate all assumptions Agents must: 1 - share ontologies, 2 - speak the same language, 3 - pre-agree on message format. • This requires: - Translating (developing mutual understanding) - Dynamically capturing a message’s semantics - Matching a message with a service 84

Match. Making System (MMS) 85

Application #6 Ontology Generation 86

TANGO: Table Analysis for Generating Ontologies § § Recognize and normalize table information Construct mini-ontologies from tables Discover inter-ontology mappings Merge mini-ontologies into a growing ontology 87

Recognize Table Information Country Afganistan Albania Population Albanian (July 2001 est. ) Orthodox 26, 813, 057 3, 510, 484 Muslim Religion Roman Shi’a Catholic Muslim 15% 20% 70% Sunni Muslim other 84% 1% 30% 88

Construct Mini-Ontology Country Afganistan Albania Population Albanian (July 2001 est. ) Orthodox 26, 813, 057 3, 510, 484 Muslim Religion Roman Shi’a Catholic Muslim 15% 20% 70% Sunni Muslim other 84% 1% 30% 89

Discover Mappings 90

Merge 91

Presentation Outline § § § § Grand Challenge Meaning, Knowledge, Information, Data Fun and Games with Data Information Extraction Ontologies Applications Limitations and Pragmatics Summary and Challenges 92

Limitations and Pragmatics § § § Data-Rich, Narrow Domain Ambiguities ~ Context Assumptions Incompleteness ~ Implicit Information Common Sense Requirements Knowledge Prerequisites … 93

Busiest Airport in 2003? Chicago - 928, 735 Landings (Nat. Air Traffic Controllers Assoc. ) - 931, 000 Landings (Federal Aviation Admin. ) Atlanta - 58, 875, 694 Passengers (Sep. , latest numbers available) Memphis - 2, 494, 190 Metric Tons (Airports Council Int’l. ) 94

Busiest Airport in 2003? Chicago - 928, 735 Landings (Nat. Air Traffic Controllers Assoc. ) - 931, 000 Landings (Federal Aviation Admin. ) Atlanta - 58, 875, 694 Passengers (Sep. , latest numbers available) Memphis - 2, 494, 190 Metric Tons (Airports Council Int’l. ) 95

Busiest Airport in 2003? Chicago - 928, 735 Landings (Nat. Air Traffic Controllers Assoc. ) - 931, 000 Landings (Federal Aviation Admin. ) Atlanta - 58, 875, 694 Passengers (Sep. , latest numbers available) Memphis - 2, 494, 190 Metric Tons (Airports Council Int’l. ) 96

Busiest Airport in 2003? Chicago - 928, 735 Landings (Nat. Air Traffic Controllers Assoc. ) - 931, 000 Landings (Federal Aviation Admin. ) Atlanta - 58, 875, 694 Passengers (Sep. , latest numbers available) Memphis - 2, 494, 190 Metric Tons (Airports Council Int’l. ) Ambiguous Whom do we trust? (How do they count? ) 97

Busiest Airport in 2003? Chicago - 928, 735 Landings (Nat. Air Traffic Controllers Assoc. ) - 931, 000 Landings (Federal Aviation Admin. ) Atlanta - 58, 875, 694 Passengers (Sep. , latest numbers available) Memphis - 2, 494, 190 Metric Tons (Airports Council Int’l. ) Important qualification 98

Dow Jones Industrial Average High Low Last Chg 30 Indus 10527. 03 10321. 35 10409. 85 +85. 18 20 Transp 3038. 15 2998. 60 3008. 16 +9. 83 15 Utils 268. 78 264. 72 266. 45 +1. 72 66 Stocks 3022. 31 2972. 94 2993. 12 +19. 65 44. 07 10, 409. 85 Graphics, Icons, … 99

Dow Jones Industrial Average High Low Last Chg 30 Indus 10527. 03 10321. 35 10409. 85 +85. 18 20 Transp 3038. 15 2998. 60 3008. 16 +9. 83 15 Utils 268. 78 264. 72 266. 45 +1. 72 66 Stocks 3022. 31 2972. 94 2993. 12 +19. 65 Weekly Daily 44. 07 Reported on same date 10, 409. 85 Implicit information: weekly stated in upper corner of page; daily not stated. 100

Presentation Outline § § § § Grand Challenge Meaning, Knowledge, Information, Data Fun and Games with Data Information Extraction Ontologies Applications Limitations and Pragmatics Summary and Challenges 101

Some Key Ideas § Data, Information, and Knowledge § Data Frames • Knowledge about everyday data items • Recognizers for data in context § Ontologies • Resilient Extraction Ontologies • Shared Conceptualizations § Limitations and Pragmatics 102

Some Research Issues § Building a library of open source data recognizers § Creating a corpora of test data for extraction, integration, table understanding, … § Precisely finding and gathering relevant information • Subparts of larger data • Scattered data (linked, factored, implied) • Data behind forms in the hidden web § Improving concept matching • Indirect matching • Calculations and unit conversions § … 103

Some Research Challenges § § Automating ontology construction Converting web data to Semantic Web data Developing effective personal software agents … www. deg. byu. edu 104