1. David W. Embley Brigham Young University Provo, Utah, USA
WoK: A Web of Knowledge
David W. Embley
Brigham Young University
Provo, Utah, USA

2. A Web of Pages  A Web of Facts
Birthdate of my great grandpa Orson
Price and mileage of red Nissans, 1990 or newer
Location and size of chromosome 17
US states with property crime rates above 1%

3. Toward a Web of Knowledge
Fundamental questions
What is knowledge?
What are facts?
How does one know?
Philosophy
Ontology
Epistemology
Logic and reasoning

4. Ontology Existence  asks “What exists?”
Concepts, relationships, and constraints

5. Epistemology
The nature of knowledge  asks: “What is knowledge?” and “How is knowledge acquired?”
Populated conceptual model

6. Logic and reasoning
Principles of valid inference – asks: “What is known?” and “What can be inferred?”
For us, it answers: what can be inferred (in a formal sense) from conceptualized data.
Find price and mileage of red Nissans, 1990 or newer

7. Logic and reasoning
Principles of valid inference – asks: “What is known?” and “What can be inferred?”
For us, it answers: what can be inferred (in a formal sense) from conceptualized data.
Find price and mileage of red Nissans, 1990 or newer

8. Making this Work  How?
Distill knowledge from the wealth of digital web data
Annotate web pages
Annotation
Annotation … …
Fact
Fact
Fact

9. Turning Raw Symbols into Knowledge
Symbols: $ 11, K Nissan CD AC
Data: price(11,500) mileage(117K) make(Nissan)
Conceptualized data:
Car(C123) has Price($11,500)
Car(C123) has Mileage(117,000)
Car(C123) has Make(Nissan)
Car(C123) has Feature(AC)
Knowledge
“Correct” facts
Provenance

10. Actualization (with Extraction Ontologies)
Find me the price and mileage of all red Nissans – I want a 1990 or newer.

11. Data Extraction

12. Semantic Annotation

13. Free-Form Query

14. Free-Form Query

15. Explanation: How it Works
Extraction Ontologies
Semantic Annotation
Free-Form Query Interpretation

16. Extraction Ontologies
Object sets
Relationship sets
Participation constraints
Lexical
Non-lexical
Primary object set
Aggregation
Generalization/Specialization
Extraction Ontology = conceptual model
Object set = collection of instances

17. Extraction Ontologies
Data Frame:
Internal Representation: float
Values
External Rep.: \s*[$]\s*(\d{1,3})*(\.\d{2})?
Left Context: $
Key Word Phrase
Key Words: ([Pp]rice)|([Cc]ost)| …
Basic Idea: Data Frames describe the value the Object Set holds
Operators
Operator: >
Key Words: (more\s*than)|(more\s*costly)|…

18. Semantic Annotation

19. Free-Form Query Interpretation
Parse Free-Form Query
(wrt data extraction ontology)
Select Ontology
Formulate Query Expression
Run Query Over Semantically Annotated Data

20. Parse Free-Form Query “Find me the and of all s – I want a ” price
mileage
red
Nissan
1996
or newer
>= Operator

21. Select Ontology
“Find me the price and mileage of all red Nissans – I want a 1996 or newer”

22. Formulate Query Expression
Conjunctive queries and aggregate queries
Mentioned object sets are all of interest.
Values and operator keywords determine conditions.
Color = “red”
Make = “Nissan”
Year >= 1996
>= Operator

23. Formulate Query Expression
Let
Where
Return

24. Run Query Over Semantically Annotated Data

25. Conclusion & Current & Future Work
Key challenge: simplicity
A simple way to annotate web pages
Simple but accurate query specification
A simple way to create extraction ontologies