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WoK: A Web of Knowledge

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

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WoK: A Web of Knowledge

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  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

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