David W. Embley Brigham Young University Provo, Utah, USA 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%

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

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

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

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

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

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

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

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

Data Extraction

Semantic Annotation

Free-Form Query

Free-Form Query

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

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

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)|…

Semantic Annotation

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

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

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

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

Formulate Query Expression Let Where Return

Run Query Over Semantically Annotated Data

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