WoK: A Web of Knowledge

1. 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: $ 11,500 117K 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

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)| … 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 For Let Where Return

24. Run QueryOver 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 www.deg.byu.edu