Download - WoK: A Web of Knowledge

David W. EmbleyBrigham Young University

Provo, Utah, USA

WoK: A Web of Knowledge

A Web of Pages A Web of FactsBirthdate 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%

• Fundamental questions– What is knowledge?– What are facts?– How does one know?

• Philosophy– Ontology– Epistemology– Logic and reasoning

Toward a Web of Knowledge

• Existence asks “What exists?”• Concepts, relationships, and constraints with

formal foundation

Ontology

• The nature of knowledge asks: “What is knowledge?” and “How is knowledge acquired?”

• Populated conceptual model

Epistemology

• 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.

Logic and Reasoning

Find price and mileage of red Nissans, 1990 or newer

• Distill knowledge from the wealth of digital web data• Annotate web pages

• Need a computational alembic to algorithmically turn raw symbols contained in web pages into knowledge

Making this Work How?

Fact

Fact

Fact

AnnotationAnnotation

…

…

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 Demo

Semantic Annotation Demo

Free-Form Query Demo

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 Ontologies

External Rep.: \s*[$]\s*(\d{1,3})*(\.\d{2})?

Key Word Phrase

Left Context: $

Data Frame:

Internal Representation: float

Values

Key Words: ([Pp]rice)|([Cc]ost)| …

Operators

Operator: >

Key Words: (more\s*than)|(more\s*costly)|…

Generality & Resiliency ofExtraction Ontologies

• Generality: assumptions about web pages– Data rich– Narrow domain– Document types

• Single-record documents (hard, but doable)• Multiple-record documents (harder)• Records with scattered components (even harder)

• Resiliency: declarative– Still works when web pages change– Works for new, unseen pages in the same domain– Scalable, but takes work to declare the extraction

ontology

Semantic Annotation

Free-Form Query Interpretation

• Parse Free-Form Query(with respect to 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”

• 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

For

Let

Where

Return

Formulate Query Expression

Run QueryOver Semantically Annotated Data

• How do we create extraction ontologies?– Manual creation requires several dozen person hours– Semi-automatic creation

• TISP (Table Interpretation by Sibling Pages)• TANGO (Table ANalysis for Generating Ontologies)• Nested Schemas with Regular Expressions• Synergistic Bootstrapping• Form-based Information Harvesting

• How do we scale up?– Practicalities of technology transfer and usage– Millions of queries over zillions of facts for thousands of

ontologies

Great!But Problems Still Need Resolution

Manual Creation

Manual Creation

-Library of instance recognizers-Library of lexicons

Automatic Annotation with TISP(Table Interpretation with Sibling Pages)

• Recognize tables (discard non-tables)• Locate table labels• Locate table values• Find label/value associations

Recognize Tables

Data Table

Layout Tables (discard)

NestedData Tables

Locate Table LabelsExamples: Identification.Gene model(s).Protein Identification.Gene model(s).2

Locate Table LabelsExamples: Identification.Gene model(s).Gene Model Identification.Gene model(s).2

12

Locate Table Values

Value

Find Label/Value AssociationsExample:(Identification.Gene model(s).Protein, Identification.Gene model(s).2) = WP:CE28918

12

Interpretation Technique:Sibling Page Comparison


Same


Almost Same


Different

Same

Technique Details

• Unnest tables• Match tables in sibling pages

– “Perfect” match (table for layout discard )– “Reasonable” match (sibling table)

• Determine & use table-structure pattern– Discover pattern– Pattern usage– Dynamic pattern adjustment

Generated RDF

WoK Demo (via TISP)

Semi-Automatic Annotation with 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

Recognize Table Information

Religion Population Albanian Roman Shi’a SunniCountry (July 2001 est.) Orthodox Muslim Catholic Muslim Muslim other

Afganistan 26,813,057 15% 84% 1%Albania 3,510,484 20% 70% 10%

Construct Mini-Ontology Religion Population Albanian Roman Shi’a SunniCountry (July 2001 est.) Orthodox Muslim Catholic Muslim Muslim other

Afganistan 26,813,057 15% 84% 1%Albania 3,510,484 20% 70% 10%

Discover Mappings

• Build a page-layout, pattern-based annotator• Automate layout recognition based on examples• Auto-generate examples with extraction

ontologies• Synergistically run pattern-based annotator &

extraction-ontology annotator

Semi-Automatic Annotation viaSynergistic Bootstrapping

(Based on Nested Schemas with Regular Expressions)

PatML Editor

Browser-Rendered Page

Page Source Text

InformationStructure Tree

Synergistic ExecutionExtraction Ontology

Document

Conceptual Annotator

(ontology-based annotation)

PartiallyAnnotated Document

Structural Annotator

(layout-driven annotation)

Annotated Document

Layout Patterns

Pattern Generation

Form-Based Information Harvesting• Forms

– General familiarity– Reasonable conceptual framework– Appropriate correspondence

• Transformable to ontological descriptions• Capable of accepting source data

• Instance recognizers– Some pre-existing instance recognizers– Lexicons

• Automated extraction ontology creation?

Form CreationBasic form-construction facilities:• single-entry field• multiple-entry field• nested form• …

Created Sample Form

Generated Ontology View

Source-to-Form Mapping

Almost Ready to Harvest

• Need reading path: DOM-tree structure• Need to resolve mapping problems

– Split/Merge– Union/Selection

Almost Ready to Harvest …



Voltage-dependent anion-selective channel protein 3VDAC-3hVDAC3Outer mitochondrial membrane Protein porin 3

Name

Almost Ready to Harvest …



Name

T-complex protein 1 subunit thetaTCP-1-thetaCCT-thetaRenal carcinoma antigen NY-REN-15

Can Now Harvest

Name

Can Now Harvest

Name

14-3-3 protein epsilonMitochondrial import stimulation factor LsubunitProtein kinase C inhibitor protein-1KCIP-114-3-3E

Can Now Harvest

Name

Voltage-dependent anion-selective channel protein 3VDAC-3hVDAC3Outer mitochondrial membrane Protein porin 3

Can Now Harvest

Name

Tryptophanyl-tRNA synthetase, mitochondrial precursorEC 6.1.1.2Tryptophan—tRNA ligaseTrpRS(Mt)TrpRS

Harvesting Populates Ontology

Harvesting Populates Ontology

Also helps adjust ontology constraints

Can Harvest from Additional Sites

Name


AutomatingExtraction Ontology Creation

Lexicons

Name

14-3-3 protein epsilonMitochondrial import stimulation factor LsubunitProtein kinase C inhibitor protein-1KCIP-114-3-3E

Name


Name

Tryptophanyl-tRNA synthetase, mitochondrial precursorEC 6.1.1.2Tryptophan—tRNA ligaseTrpRS(Mt)TrpRS

…14-3-3 protein epsilonMitochondrial import stimulation factor LsubunitProtein kinase C inhibitor protein-1KCIP-114-3-3E…T-complex protein 1 subunit thetaTCP-1-thetaCCT-thetaRenal carcinoma antigen NY-REN-15…Tryptophanyl-tRNA synthetase, mitochondrial precursorEC 6.1.1.2Tryptophan—tRNA ligaseTrpRS(Mt)TrpRS…

AutomatingExtraction Ontology Creation

Instance RecognizersNumber Patterns Context Keywords and Phrases

Automatic Source-to-Form Mapping

Automatic Semantic Annotation

Recognize and annotate with respect to an ontology

• Advanced free-form queries with disjunction and negation

• Form-based query language• Table-based query languages• Graphical query languages

Practicalities: WoK Query Interfaces(Future Work)

• Won’t just happen without sufficient content• Niche applications

– Historical Data (e.g. Genealogy)– Topical Blogs

• Local WoKs– Intra-organizational effort– Individual interests

Practicalities: Bootstrapping the WoK(Future Work)

• Potential Rapid growth– Thousands of ontologies– Millions of simultaneous queries– Billions of annotated pages– Trillions of facts

• Search-engine-like caching & query processing

Practicalities: Scalability(Future Work)

• Automatic (or near automatic) creation of extraction ontologies

• Automatic (or near automatic) annotation of web pages

• Simple but accurate query specification without specialized training

Key to Success:Simplicity via Automation

www.deg.byu.edu