AQUAINT: Two-Way Bridge

AQUAINT

A Two-way Bridge between Language and LogicRecent Accomplishments and Challenges

Danny Bobrow and Ron KaplanWith

Cleo Condoravdi, Dick Crouch, Valeria de Paiva, Lauri Karttunen, Tracy King, John Maxwell, Annie Zaenen

AQUAINT: Two-Way Bridge

AQUAINT

Outline

• Recent accomplishments• Some technical details• Collaborative challenges

Assertions

F-SC-S KR

XLE/LFG Parsing KR Mapping Target LogicText

M

Text

Sources

Question Query

Text to user

ComposedF-Structure Templates

MM

AnswersExplanationsSubqueries

XLE/LFGGeneration

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Accomplishments: Incorporation of external resources

• Cyc extractions– Generalization hierarchy – Language to KR mappings

• VerbNet extractions– Syntactic lexicon, subcategorization frames– Thematic roles and semantics– Linkage between syntax and semantics

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Accomplishments:Test suites

• XLE syntactic coverage with gold standards• Extracted examples: WordNet, VerbNet, NIST• PropBank

mapping to full sentence, dependency triples

(including stemmed verbs & argument heads)

• Project specific– Key phenomena

context inducing constructions, questions, time expressions, etc

– Domainsterrorism (bombings and money laundering),

travel, commercial transactions

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Accomplishments: System Work

• PARC advances– Semantics-KR mapping in term-rewriting system – XLE, Glue, term-rewriting in one process– Uniform use of XLE packed ambiguity management

• Exploration and exploitation– ResearchCyc– ANSProlog– WordNet

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PARC ArchitectureText

C-structure

F-structure

Linguisticsemantics

Conceptual&

ContextedKR

(Bridge KR)

XLE/LFGparsing

Gluederivation

Semanticlexicon

LFG GrammarLexicon

TermRewriting

Word structureEntity recognition Morphology

Target KRR

Mapping Rules

Cyc

VerbNetWordNet

CycANSProlog

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Layered Mapping from F-Structures to KR

• Glue derivation: F-structure to linguistic semantics– Captures intensionality, scope variation, etc

– Shows logical structure by nesting of formulas

• Linguistic semantics to flattened clausal form– Use skolems as terms to encode structure

– Bridges between linguistic structure and knowledge structure

• Map semantic clauses to Bridge KR clauses– Use resourced term-rewriting with ambiguity management

– Canonicalizes to more uniform (easier to match) representations

• Map from Bridge KR to target KR

Natural decomposition through intermediate representations

with different formal characteristicsthat capture different generalizations

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Glue DerivationF-Structure to Linguistic Semantics

• Two parallel, linked logics– Resourced linear logic for guiding semantic construction

• Derivation driven solely by F-structures as types– A meaning logic for semantic representation

• Composition driven by linear logic derivation

• Alternative derivations introduce semantic ambiguities– Quantifier scope ambiguity

• Every cable is attached to a base-plate x cable(x) y plate(y) & attached(x,y) y plate(y) & x cable(x) attached(x,y)

– Internal/external modification • John put up bookshelves for two days

– Working on it for two days, or stayed up for two days– Comparison sets in information structure

• Pets must be carried on escalator• Clothes must be worn in public

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Linguistic semantics and flattened clauses

Two terrorists plan to attack a factory

A1,B1: exists(f. factory(f) & exists2(t. terrorist(t) & plan(t, exists(a. attack(a, t ,f))))One real factory for both terrorists

A1,B2: exists2(t. terrorist(t) & exists(f. factory(f) & plan(t, exists(a. attack(a, t ,f)))))A different real factory for each terrorist

A2: exists2(t. terrorist(t) & plan(t, exists(f. factory(f) & exists(a. attack(a, t, f)))))The terrorists haven’t chosen factories

Result of glue derivation

ist(c0, terrorist(t)) ist(c0, cardinality(t, 2))

ist(c0, plan(t, c1)) ist(c1, attack(a, t, f))

skfn(t, c0) skfn(c1, t) skfn(a, c1)

A1: ist(c0, factory(f))B1: skfn(f,c0)B2: skfn(f, t)A2: skfn(f, c1)A2: ist(c1, factory(f))

c0 is the top level contextc1 is the context of the planist(C,X): X is true in context Cskfn(sk, arg): skolem sk depends on arg

Flat, packed semantics

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Skolems refer to subconcepts• terrorist(t)

– t does not refer to an individual that is a terrorist– rather t refers to a subconcept of the concept

“terrorist”

• Permits a simpler treatment of intensional constructions– Negotiations prevented a war n. subconcept(n, Negotiation) &

w. subconcept(w, War) & prevent(n,w)

– prevent(n,w) means that • concept n has instances (in the world of the prevention)• concept w has no instances in that world

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After Glue and Flattening:Mapping to Bridge KR

• Map to concepts & roles in ontology (Cyc)

• Apply meaning postulates / lexical entailments

• Make conceptual & contextual structure explicit

• Eliminate ontologically ill-formed analyses

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Example Abstract KR (I)Words Cyc Terms & Roles

• Ed got to Baghdad(objectMoving get_ev5 Ed7)

(toLocation get_ev5 Baghdad6)

(sub_concept get_ev5 Conveying-Generic)

(sub_concept Baghdad6 CityOfBaghdadIraq)

(sub_concept Ed7 Ed-default-name)

(instantiated t Ed7) (instantiated t Baghdad6) (instantiated t get_ev5)

• Ed got the package to Baghdad(objectMoving get_ev8 package9)

(toLocation get_ev8 Baghdad10)

(doneBy get_ev8 Ed11) …

• Ed got the package(objectMoving get_ev12 package14)

(toLocation get_ev12 Ed13) …

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• Ed cooled the room(sub_concept cool_ev18 scalar-state-change)(decreasesCausally cool_ev18 room20 temperatureOfObject)(doneBy cool_ev18 Ed21)(sub_concept room20 RoomInAConstruction) …

• Ed lowered the temperature of the room(sub_concept lower_ev22 scalar-state-change)(decreasesCausally lower_ev22 room24 temperatureOfObject)(doneBy lower_ev22 Ed23)(sub_concept room24 RoomInAConstruction) …

• The room cooled(sub_concept cool_ev25 scalar-state-change)(decreasesCausally cool_ev25 room26 temperatureOfObject))(sub_concept room26 RoomInAConstruction) …

Example Abstract KR (II)Canonicalization

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Example Abstract KR (III)Contextual Structure

• The senator prevented a warpreventRelation (action1 c0 c2)doneBy (action1 senator3)

sub_concept (action1 Eventuality)sub_concept (senator3 USSenator)sub_concept (war4 WagingWar)

instantiated (c0 senator3)instantiated (c0 action1)uninstantiated (c0 war2)

uninstantiated (c2 action1)instantiated (c2 war2)

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Term-Rewriting for KR Mapping

Example Rule

• Rules of form– <Input terms> ==> <Output terms> (obligatory)– <Input terms> ?=> <Output terms> (optional)

(optional rules introduces new choices)

• Input patterns allow– Consume term if matched: Term– Test on term without consumption: +Term– Test that term is missing: -Term– Procedural attachment: {ProcedureCall}

hire(E), subj(E, X), obj(E, Y),

+sub_concept(X, CX), +sub_concept(Y, CY),

{genls(CX, Organization), genls(CY, Person)}

==> sub_concept(E, EmployingEvent),

performedBy(E,X), personEmployed(E,Y).

• Ordered rule application– Rule1 applied in all possible ways to Input to produce Output1– Rule2 applied in all possible ways to Output1 to produce Output2

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• Alternative lexical mappings /- cyc_concept_map(bank, FinancialInstitution).

/- cyc_concept_map(bank, SideOfRiver).

ist(C, P(Arg)), cyc_concept_map(P,Concept) ==> sub_concept(Arg,Concept).

• Input term– ist(c0, bank(b1))

• Alternative rule applications produce different outputs Rewrite system represents this ambiguity by a new choice

• Output– C1: sub_concept(b1, FinancialInstitution)

C2: sub_concept(b1, SideOfRiver)– (C1 xor C2) 1

Term-rewriting can introduce ambiguity

PermanentBackgroundFacts

Mapping fromPredicate to Cyc concept

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• The bank hired Ed hire(E), subj(E,X), obj(E,Y),

+sub_concept(X,CX), +sub_concept(Y,CY),

{genls(CX, Organization), genls(CY,Person)}

==> sub_concept(E, EmployingEvent),

performedBy(E,X), personEmployed(E,Y).

• From Cyc: genls(FinancialInstitution, Organization) true genls(SideOfRiver, Organization) false

• If bank is mapped to SideOfRiver, the rule will not fire.This leads to a failure to consume the subject. subj(A,B) ==> stop.

prunes this analysis from the choice space.

• In general, later rewrites prune analyses that don’t consume grammatical roles.

Term-rewriting can prune ill-formed mappings

Rule for mappinghire

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Mapping to Target KR: Collaborative Challenges

• Target ontology– Cyc – extending the ontology– ANSProlog -- mapping from Cyc ontology

• Packed ambiguity– Pass on packed ambiguities? – Unpack possible interpretations?– Choose statistically most likely?

• Contexts– Cyc: will microtheories work?– ANSProlog: incorporating a logic of contexts?

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Two forms of future evaluation

• Project– Regression test suites to ensure progress towards

benchmark representations– Matching algorithm to test utility of KR canonicalization

• AQUAINT Program: – Local textual inference?– Incorporation by a back-end system?

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Thank you