theoretical foundations for enabling a web of knowledge
DESCRIPTION
Theoretical Foundations for Enabling a Web of Knowledge. David W. Embley Andrew Zitzelberger Brigham Young University. www.deg.byu.edu. 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 - PowerPoint PPT PresentationTRANSCRIPT
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Theoretical Foundations for Enabling a Web of Knowledge
David W. EmbleyAndrew Zitzelberger
Brigham Young University
www.deg.byu.edu
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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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• Fundamental questions– What is knowledge?– What are facts?– How does one know?
• Philosophy– Ontology– Epistemology– Logic and reasoning
Toward a Web of Knowledge
(a computational view)
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• Existence—asks “What exists?”• Concepts, relationships, and constraints
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?”
• Justified, inference from conceptualized data (reasoning chain, grounded in source)
Logic and Reasoning
Find price and mileage of red Nissans, 1990 or newer
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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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WoK Foundation Details• Objectives
– Establish formal WoK foundation (can it work?)– Enable WoK construction tools (can it be built?)
• WoK Vision Practicalities– Simplicity– Scalability– Spin-off
• Extraction ontologies• Free-form query processing• Knowledge bundles• Knowledge-bundle building tools• …
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WoK Knowledge Bundle (KB) Formalization
KB: a 7-tuple: (O, R, C, I, D, A, L)– O: Object sets—one-place predicates– R: Relationship sets—n-place predicates– C: Constraints—closed formulas– I: Interpretations—predicate calc. models for (O, R, C)– D: Deductive inference rules—open formulas– A: Annotations—links from KB to source documents– L: Linguistic groundings—data frames
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KB: (O, R, C, …)
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KB: (O, R, C, …)
O: one-place predicates: DeceasedPerson(x), Age(x), …R: n-place predicates: DeceasedPerson(x)hasAge(y), …C: constraints: x(DeceasedPerson(x) 1y(DeceasedPerson(x)hasAge(y)) …
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KB: (O, R, C, I, …) Age(69)DeceasedPerson(x37)DeceasedPerson(x37)hasAge(69)
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Aside #1: Decidability & Tractability
• Mapping to OWL-DL• Also to ALCN
– ALCN Tableaux Calculus– Decidable, PSPACE-complete
• Enforce integrity constraints in DB fashion
• Further exploration– Complexity of the particular FOL fragment for KBs– Adjustments to conceptual-modeling features?
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Aside #2: Metamodel(in terms of itself)
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KB: (O, R, C, I, …, L)
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KB: (O, R, C, I, …, A, L)
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KB: (O, R, C, I, D, A, L)
Brother(y, z) :- DeceasedPerson(x)hasRelationship(‘son’)toRelativeName(y), DeceasedPerson(x)hasRelationship(‘son’)toRelativeName(z), y != z.
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KB Query
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KB Query
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Web of Knowledge (WoK)• Plato: “justified true belief”• Facts
– Extensional (grounded to source)– Intentional (exposed reasoning chains)
• Knowledge Bundle (KB)– Populated ontology– Superimposed over web documents
• Web of Knowledge: interconnected KBs– Instance equality links– Class equality links
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WoK Construction Tools• Automatic Construction• Semi-Automatic Construction• Construction via Semantic Integration
– Semantic enrichment– Schema mapping– Record linkage
• Construction via Extraction Ontologies• Synergistic Construction
– You “pay-as-you-go”– It “learns-as-it-goes”
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Transformation Principles• 5-tuple: (R, S, T, , )
– R: Resources– S: Source– T: Target– : Procedural transformation– : Non-procedural transformation
• Information & Constraint Preservation– Procedure exists to compute S from T– CT C⇒ S (constraints of T imply constraints of S)
(KB: Knowledge Bundle)
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Construction: Reverse Engineering(Formal Data Structures)
XML Schema C- XML
Also for RDB, OWL/RDF, …
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Construction: Reverse Engineering(Nested Tables)
Table interpretation needed
…
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Construction with TISP:Table Interpretation by Sibling Pages
Same
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Different
Same
Construction with TISP:Table Interpretation by Sibling Pages
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Construction with TISP:Table Interpretation by Sibling Pages
…
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fleck velter
gonsity (ld/gg)
hepth(gd)
burlam 1.2 120
falder 2.3 230
multon 2.5 400
repeat:1. understand table2. generate mini-ontology3. match with growing ontology4. adjust & mergeuntil ontology developed
Construction via Semantic IntegrationTANGO: Table ANalysis for Generating Ontologies
velter
hepth
gonsityfleck
1has 1:*
1has 1:*
velter
hepth
gonsityfleck
1has 1:*
1has 1:*
GrowingOntology
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Vertical-cut-first notatioin: [{ [C D ][C1 {D1 D2 }][C2 {D1 D2 }]} {A [{A1 [A11A12 ]}A2 ][d11 d12 d13] [d21 d22 d23 ][d31 d32 d33 ][d41 d42 d43 ]}].Category notation:(A,{(A1,{(A11,F),(A12,F)}),(A2,F)})(C, {(C1,F),(C2,F)})(D, {(D1,F),(D2,F)})Delta notation:d({A.A1.A11,C.C1,D.D1}) = d11d({A.A1.A12,C.C1,D.D1}) = d12...
C D A11 A12D1 d11 d12D2 d21 d22D1 d31 d32D2 d41 d42
AA1
A2
C1 d13d23
C2 d33d43
Table Analysis
A C D
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Semantic Enrichment
• Semantic information lost in abstraction– Concepts– Relationships– Constraints
• Recovery via outside resources– WordNet– Data-frame library
• Example …
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Sample Input Region and State Information
Location Population (2000) Latitude LongitudeNortheast 2,122,869 Delaware 817,376 45 -90 Maine 1,305,493 44 -93Northwest 9,690,665 Oregon 3,559,547 45 -120 Washington 6,131,118 43 -120
Location
Northeast Northwest
Maine WashingtonOregonDelaware
[Dimension2]
LongitudeLatitudePopulation
2,122,869 -120817,376
Title: Region and State Information
2000
Sample Output
Semantic Enrichment Example
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Concept/Value Recognition• Lexical Clues
– Labels as data values– Data value assignment
• Data Frame Clues– Labels as data values– Data value assignment
• Default– Recognize concepts and
values by syntax and layout
Location
Northeast Northwest
Maine WashingtonOregonDelaware
[Dimension2]
LongitudeLatitudePopulation
2,122,869 -120817,376
Title: Region and State Information
2000
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Concept/Value Recognition• Lexical Clues
– Labels as data values– Data value assignment
• Data Frame Clues– Labels as data values– Data value assignment
• Default– Recognize concepts and
values by syntax and layout
Location
Northeast Northwest
Maine WashingtonOregonDelaware
[Dimension2]
LongitudeLatitudePopulation
2,122,869 -120817,376
Title: Region and State Information
2000
Concepts and Value Assignments
NortheastNorthwest
DelawareMaineOregonWashington
Location Region State
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Concept/Value Recognition• Lexical Clues
– Labels as data values– Data value assignment
• Data Frame Clues– Labels as data values– Data value assignment
• Default– Recognize concepts and
values by syntax and layout
Population Latitude Longitude
2,122,869817,3761,305,4939,690,6653,559,5476,131,118
45444543
-90-93-120-120
Year
20022003
Location
Northeast Northwest
Maine WashingtonOregonDelaware
[Dimension2]
LongitudeLatitudePopulation
2,122,869 -120817,376
Title: Region and State Information
2000
Concepts and Value Assignments
NortheastNorthwest
DelawareMaineOregonWashington
Location Region State
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Location
Northeast Northwest
Maine WashingtonOregonDelaware
[Dimension2]
LongitudeLatitudePopulation
2,122,869 -120817,376
Title: Region and State Information
2000
Relationship Discovery• Dimension Tree Mappings• Lexical Clues
– Generalization/Specialization– Aggregation
• Data Frames• Ontology Fragment Merge
Location
Northeast Northwest
Maine WashingtonOregonDelaware
[Dimension2]
LongitudeLatitudePopulation
2,122,869 -120817,376
Title: Region and State Information
2000
2000
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Relationship Discovery• Dimension Tree Mappings• Lexical Clues
– Generalization/Specialization– Aggregation
• Data Frames• Ontology Fragment Merge
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Constraint Discovery• Generalization/Specialization• Computed Values• Functional Relationships• Optional Participation
Region and State InformationLocation Population (2000) Latitude LongitudeNortheast 2,122,869 Delaware 817,376 45 -90 Maine 1,305,493 44 -93Northwest 9,690,665 Oregon 3,559,547 45 -120 Washington 6,131,118 43 -120
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Mapping and Merging
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Mapping and Merging
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Mapping and Merging
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Mapping and Merging
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Mapping and Merging
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Mapping and Merging
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Automated Schema Matching
• Central Idea: Exploit All Data & Metadata• Matching Possibilities (Facets)
– Attribute Names– Data-Value Characteristics– Expected Data Values– Data-Dictionary Information– Structural Properties
• Direct & Indirect Matching
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Expected Data Values
Make
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Direct & Indirect Schema Mappings
Source
Car
Year
Cost
Style
YearFeature
Cost
Phone
Target
Car
MilesMileage
Model
Make Make&
Model
Color
Body Type
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Ontological Record Linkage
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Construction with FOCIH: (Form-based Ontology Creation and Information Harvesting)
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Construction with FOCIH:(Form-based Ontology Creation and Information Harvesting)
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Ontology GenerationCzech RepublicGermanyFrance…
PragueBerlinParis…
78,866.00 sq km551,695.00 sq km357,114.22 sq km…
atheistRoman CatholicProtestantOrthodoxother…
10,264,212 2001 8,015,315 2050…
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Construction withExtraction Ontology Editor
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Synergistic ConstructionKnowledge Begets Knowledge
Czech RepublicGermanyFrance…
PragueBerlinParis…
sq kmdata-frame recognizer
Population-Yeardata-frame recognizer
atheistRoman CatholicProtestantOrthodoxother…
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Synergistic ConstructionYou “pay-as-you-go” / It “learns-as-it-goes”
Czech RepublicGermanyFrance…
PragueBerlinParis…
sq kmdata-frame recognizer
Population-Yeardata-frame recognizer
atheistRoman CatholicProtestantOrthodoxother…
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WoK Usage Tools
• Based on “Understanding”• “Read” / “Write”• Applications
– Free-form query processing– Reasoning chains grounded in annotated instances– Knowledge augmentation– Research studies
“Understanding”:• S: Source Conceptualization• T: Target Conceptualization (formalized as a KB)• If there exists an S-to-T transformation:
– One-place & n-place predicates– Facts (wrt predicates)– Operations– Constraints of T all hold
S: Usually not formal;makes “understanding”difficult (& interesting)
But: Linguistically grounded KBsare also extraction ontologies,that can construct mappings.
“Understanding” is the mapping; “reading” constructs the mapping;“writing” explains the mapping in its own words.
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Free-form Query Processing with Annotated Results
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Alerter for www.craigslist.org
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Alerter for www.craigslist.org
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Alerter for www.craigslist.org
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Alerter for www.craigslist.org
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Reasoning ChainsGrounded in Annotated Instances
FamilySearch.org – Indexing250 Million+ records indexed
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Reasoning ChainsGrounded in Annotated Instances
FamilySearch.org – Indexing250 Million+ records indexed
Person(x)isHusbandOfPerson(y) :- Person(x), Person(y), Person(x)hasGender(‘Male’), Person(x)hasRelationToHead(‘Head’),
Person(y)hasRelationToHead(‘Wife’), Person(x)isInSameFamilyAsPerson(y).Person(x)isInSameFamilyAsPerson(y) :-
Person(x)hasFamilyNumber(z)inCensusRecord(w), Person(y)hasFamilyNumber(z)inCensusRecord(w).
Person(x)named(y)isHusbandOfPerson(z)named(w) :- Person(x)isHusbandOfPerson(z), Person(x)hasName(y), Person(z)hasName(w).
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Reasoning ChainsGrounded in Annotated Instances
FamilySearch.org – Indexing250 Million+ records indexed
Person(x)isHusbandOfPerson(y) :- Person(x), Person(y), Person(x)hasGender(‘Male’), Person(x)hasRelationToHead(‘Head’),
Person(y)hasRelationToHead(‘Wife’), Person(x)isInSameFamilyAsPerson(y).Person(x)isInSameFamilyAsPerson(y) :-
Person(x)hasFamilyNumber(z)inCensusRecord(w), Person(y)hasFamilyNumber(z)inCensusRecord(w).
Person(x)named(y)isHusbandOfPerson(z)named(w) :- Person(x)isHusbandOfPerson(z), Person(x)hasName(y), Person(z)hasName(w).
Who is the husband of Mary Bryza?
Husband Name Wife Name … John Bryza Mary Bryza …
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Reasoning ChainsGrounded in Annotated Instances
FamilySearch.org – Indexing250 Million+ records indexed
Person(x)isHusbandOfPerson(y) :- Person(x), Person(y), Person(x)hasGender(‘Male’), Person(x)hasRelationToHead(‘Head’),
Person(y)hasRelationToHead(‘Wife’), Person(x)isInSameFamilyAsPerson(y).Person(x)isInSameFamilyAsPerson(y) :-
Person(x)hasFamilyNumber(z)inCensusRecord(w), Person(y)hasFamilyNumber(z)inCensusRecord(w).
Person(x)named(y)isHusbandOfPerson(z)named(w) :- Person(x)isHusbandOfPerson(z), Person(x)hasName(y), Person(z)hasName(w).
Who is the husband of Mary Bryza?
Husband Name Wife Name … John Bryza Mary Bryza …
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Reasoning ChainsGrounded in Annotated Instances
FamilySearch.org – Indexing250 Million+ records indexed
Person(x)isHusbandOfPerson(y) :- Person(x), Person(y), Person(x)hasGender(‘Male’), Person(x)hasRelationToHead(‘Head’),
Person(y)hasRelationToHead(‘Wife’), Person(x)isInSameFamilyAsPerson(y).Person(x)isInSameFamilyAsPerson(y) :-
Person(x)hasFamilyNumber(z)inCensusRecord(w), Person(y)hasFamilyNumber(z)inCensusRecord(w).
Person(x)named(y)isHusbandOfPerson(z)named(w) :- Person(x)isHusbandOfPerson(z), Person(x)hasName(y), Person(z)hasName(w).
Who is the husband of Mary Bryza?
Husband Name Wife Name … John Bryza Mary Bryza …
Person(p1) named(‘John Bryza’) is husband of Person(p2) named(‘Mary Bryza’)because: Person(p1) is husband of Person(p2) and Person(p1) has Name(‘John Bryza’) and Person(p2) has Name(‘Mary Bryza’);and Person(p1) is husband of Person(p2)because: Person(p1) has gender(‘Male’) and Person(p1) has relation to Head(‘Head’), and Person(p2) has relation to Head(‘Wife’) and Person(p1) is in same family as Person(p2).and Person(p1) is in same family as Person(p2)because: Person(p1) has family number(80) in Census Record(r1) and Person(p2) has family number(80) in Census Record(r1).
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Reasoning Decidability & Tractability
• “… extending OWL-DL with safe, positive Datalog rules preserves decidability of reasoning.” [Rosati, JWS05]
• “… answering conjunctive queries (a.k.a. select-project-join queries) under DL-Lite … is polynomial …” [Cali,Gottlob,Pieris, ER09]
• Further exploration– Adjustments as issues are better understood– Example: negation – “… guarded Datalog is PTIME-complete
…” [Cali,Gottlob,Lukasievicz, DL09]
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Knowledge Augmentation (TANGO)
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% 30%
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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% 30%
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Discover Mappings
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Mergeresulting in augmented knowledge
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Fact Finding and Organizationfor Research Studies
• Example: A Bio-Research Study• Objective: Study the association of:
– TP53 polymorphism and– Lung cancer
• Task: Locate, Gather, Organize Data from:– Single Nucleotide Polymorphism database– Medical journal articles– Medical-record database
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Gather SNP Information from the NCBI dbSNP Repository
SNP: Single Nucleotide PolymorphismNCBI: National Center for Biotechnology Information
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Search PubMed Literature
PubMed: Search-engine access to life sciences and biomedical scientific journal articles
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Reverse-Engineer Human Subject Information from INDIVO
INDIVO: personally controlled health record system
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Reverse-Engineer Human Subject Information from INDIVO
INDIVO: personally controlled health record system
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Add Annotated Images
Radiology Report(John Doe, July 19, 12:14 pm)
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Query and Analyze Data in Knowledge Bundle
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Summary, Conclusions & Future Work• WoK Vision
– Formalism: “as simple as possible, but no simpler”– Valuable subcomponents
• Extraction ontologies (IR, alerter, search-engine enhancement)• Reverse engineering (for understanding, for redesign and deployment)• Knowledge bundles (for research studies, for sharing knowledge)• Truth authentication (annotation, reasoning chains, provenance)
• Scalability Issues– System performance
• Decidable & tractable• Parallel-processing opportunities
– Human input requirements• Semi-automatic—burden shifted as much as possible to the system• Synergistic incremental construction
– You “pay as you go”– It “learns as it goes”
www.deg.byu.edu