aquaint aquaint: two-way bridge a two-way bridge between language and logic recent accomplishments...

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

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


AQUAINT

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


AQUAINT

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?


AQUAINT

Thank you

aquaint aquaint: two-way bridge a two-way bridge between language and logic recent accomplishments...

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