WoK: A Web of Knowledge
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Transcript of WoK: A Web of Knowledge
![Page 1: WoK: A Web of Knowledge](https://reader037.fdocuments.net/reader037/viewer/2022110210/56812e06550346895d936d73/html5/thumbnails/1.jpg)
David W. EmbleyBrigham Young University
Provo, Utah, USA
WoK: A Web of Knowledge
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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%
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• Fundamental questions– What is knowledge?– What are facts?– How does one know?
• Philosophy– Ontology– Epistemology– Logic and reasoning
Toward a Web of Knowledge
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• Existence asks “What exists?”• Concepts, relationships, and constraints with
formal foundation
Ontology
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• The nature of knowledge asks: “What is knowledge?” and “How is knowledge acquired?”
• Populated conceptual model
Epistemology
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• 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
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• 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
…
…
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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($11,500)– Car(C123) has Mileage(117,000)– Car(C123) has Make(Nissan)– Car(C123) has Feature(AC)
• Knowledge– “Correct” facts– Provenance
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Actualization (with Extraction Ontologies)
Find me the price and mileage of all red Nissans – I want a 1990 or newer.
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Data Extraction Demo
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Semantic Annotation Demo
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Free-Form Query Demo
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Explanation: How it Works
• Extraction Ontologies• Semantic Annotation• Free-Form Query Interpretation
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Extraction Ontologies
Object sets
Relationship sets
Participation constraints
Lexical
Non-lexical
Primary object set
Aggregation
Generalization/Specialization
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Extraction Ontologies
External Rep.: \s*[$]\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)|…
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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
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Semantic Annotation
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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
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Parse Free-Form Query “Find me the and of all s – I want a ”
price
mileage
red
Nissan
1996
or newer
>= Operator
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Select Ontology“Find me the price and mileage of all red Nissans – I want a 1996 or newer”
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• 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
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For
Let
Where
Return
Formulate Query Expression
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Run QueryOver Semantically Annotated Data
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• 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
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Manual Creation
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Manual Creation
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Manual Creation
-Library of instance recognizers-Library of lexicons
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Automatic Annotation with TISP(Table Interpretation with Sibling Pages)
• Recognize tables (discard non-tables)• Locate table labels• Locate table values• Find label/value associations
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Recognize Tables
Data Table
Layout Tables (discard)
NestedData Tables
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Locate Table LabelsExamples: Identification.Gene model(s).Protein Identification.Gene model(s).2
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Locate Table LabelsExamples: Identification.Gene model(s).Gene Model Identification.Gene model(s).2
12
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Locate Table Values
Value
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Find Label/Value AssociationsExample:(Identification.Gene model(s).Protein, Identification.Gene model(s).2) = WP:CE28918
12
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Interpretation Technique:Sibling Page Comparison
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Interpretation Technique:Sibling Page Comparison
Same
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Interpretation Technique:Sibling Page Comparison
Almost Same
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Interpretation Technique:Sibling Page Comparison
Different
Same
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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
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Generated RDF
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WoK Demo (via TISP)
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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
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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%
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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%
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Discover Mappings
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Merge
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• 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)
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PatML Editor
Browser-Rendered Page
Page Source Text
InformationStructure Tree
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Synergistic ExecutionExtraction Ontology
Document
Conceptual Annotator
(ontology-based annotation)
PartiallyAnnotated Document
Structural Annotator
(layout-driven annotation)
Annotated Document
Layout Patterns
Pattern Generation
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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?
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Form CreationBasic form-construction facilities:• single-entry field• multiple-entry field• nested form• …
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Created Sample Form
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Generated Ontology View
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Source-to-Form Mapping
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Source-to-Form Mapping
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Source-to-Form Mapping
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Source-to-Form Mapping
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Almost Ready to Harvest
• Need reading path: DOM-tree structure• Need to resolve mapping problems
– Split/Merge– Union/Selection
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Almost Ready to Harvest …
• Need reading path: DOM-tree structure• Need to resolve mapping problems
– Split/Merge– Union/Selection
Voltage-dependent anion-selective channel protein 3VDAC-3hVDAC3Outer mitochondrial membrane Protein porin 3
Name
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Almost Ready to Harvest …
• Need reading path: DOM-tree structure• Need to resolve mapping problems
– Split/Merge– Union/Selection
Voltage-dependent anion-selective channel protein 3VDAC-3hVDAC3Outer mitochondrial membrane Protein porin 3
Name
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Almost Ready to Harvest …
• Need reading path: DOM-tree structure• Need to resolve mapping problems
– Split/Merge– Union/Selection
Name
T-complex protein 1 subunit thetaTCP-1-thetaCCT-thetaRenal carcinoma antigen NY-REN-15
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Almost Ready to Harvest …
• Need reading path: DOM-tree structure• Need to resolve mapping problems
– Split/Merge– Union/Selection
Name
T-complex protein 1 subunit thetaTCP-1-thetaCCT-thetaRenal carcinoma antigen NY-REN-15
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Can Now Harvest
Name
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Can Now Harvest
Name
14-3-3 protein epsilonMitochondrial import stimulation factor LsubunitProtein kinase C inhibitor protein-1KCIP-114-3-3E
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Can Now Harvest
Name
Voltage-dependent anion-selective channel protein 3VDAC-3hVDAC3Outer mitochondrial membrane Protein porin 3
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Can Now Harvest
Name
Tryptophanyl-tRNA synthetase, mitochondrial precursorEC 6.1.1.2Tryptophan—tRNA ligaseTrpRS(Mt)TrpRS
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Harvesting Populates Ontology
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Harvesting Populates Ontology
Also helps adjust ontology constraints
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Can Harvest from Additional Sites
Name
T-complex protein 1 subunit thetaTCP-1-thetaCCT-thetaRenal carcinoma antigen NY-REN-15
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AutomatingExtraction Ontology Creation
Lexicons
Name
14-3-3 protein epsilonMitochondrial import stimulation factor LsubunitProtein kinase C inhibitor protein-1KCIP-114-3-3E
Name
T-complex protein 1 subunit thetaTCP-1-thetaCCT-thetaRenal carcinoma antigen NY-REN-15
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…
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AutomatingExtraction Ontology Creation
Instance RecognizersNumber Patterns Context Keywords and Phrases
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Automatic Source-to-Form Mapping
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Automatic Semantic Annotation
Recognize and annotate with respect to an ontology
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• 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)
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• 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)
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• 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)
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• 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