QUIRK:Project Progress Report Monterey, June 11-13 2002

Cycorp IBM

Task decomposition

• Question understanding– English question to CycL query

• Question answering– CycL Query to CycL bindings and proof trees

• Answer ranking and presentation– CycL bindings and proof trees to cognitively reasonable English

rendition

Knowledge-based query decomposition

(grandfathers (PersonNamedFn “Charles Bonaparte”) ?GRAND)

(and

(parents (PersonNamedFn “Charles Bonaparte”) ?PARENT)

(father ?PARENT ?GRAND))

(?PARENT . “Jerome Napoleon Bonaparte”)

(?GRAND . (PersonNamedFn “Jerome Bonaparte”)

Integration of heterogeneous sources

We have achieved integration of:

• Knowledge representation (Cyc’s Knowledge Base)

• Textual documents (TREC corpus via GuruQA search engine)

• Databases (NIMA, USGS)

CycL to GuruQA translator

• Implemented query language object data-structures

• Of which the GuruQA syntactic objects are instances

• Can be subclassed to target other IR engines

CycL to GuruQA translator

(numberOfInhabitants CityOfRomeItaly ?NUM)

@WIN(1

@SYN(Rome Roma)

Italy

population

POPULATE$)

CycL to GuruQA translator

(businessContacts BillClinton ?WHO)

@EXCWIN(1

@SYN(

@PHR(0 business associate)

@PHR(0 business partner))

*Bill_Clinton

PERSON$)

Answer selector• Implemented so as to be able to access Textract and other

existing or future (text analysis) services

• receives as input– original CycL query

– TREC paragraph retrieved by GuruQA

• returns a list of– CycL bindings (reified or virtual)

– their score

Answer selectorQUERY: (employees ?ORG (PersonNamedFn “Mary Jones”))

Step 1: learns new, typed, vocabulary from Textract

“Mary Jones’ office at ACME Inc has a lovely view of the bridge over the Moldava as her previous office at SUPRA Ltd did.”

(PersonNamedFn “Mary Jones”) ====isa==> Person

(CompanyNamedFn “ACME, Inc”) ==isa==> Company

(RiverNamedFn “Moldava”) =======isa==> River

(CompanyNamedFn “SUPRA Ltd”) ==isa==> Company

Answer selector

QUERY: (employees ?ORG (PersonNamedFn “Mary Jones”))

Step 2: Retrieves CycL interpretations from paragraph

“Mary Jones’ office at ACME Inc has a lovely view of the bridge over the Moldava as her previous office at SUPRA Ltd did.”

(PersonNamedFn “Mary Jones”) OfficeRoom

(CompanyNamedFn “ACME, Inc”) Bridge

(RiverNamedFn “Moldava”)

(CompanyNamedFn “SUPRA Ltd”)

Answer selectorStep 3: filter answers by predicate argument constraints

Q: (employees ?ORG (PersonNamedFn “Mary Jones”))

Organization Person

(CompanyNamedFn “ACME, Inc”)

(CompanyNamedFn “Supra, Ltd”)

Bridge

OfficeRoom

(RiverNamedFn “Moldava”)

(PersonNamedFn “Mary Jones”)

Answer selectorStep 4: rank retrieved bindings by their proximity to the ground

terms from the CycL query in the paragraph.

QUERY: (employees ?ORG (PersonNamedFn “Mary Jones”))

“Mary Jones’ office at ACME Inc has a lovely view of the bridge over the Moldava as her previous office at SUPRA Ltd did.”

(CompanyNamedFn “ACME Inc”) => 3 words away

(CompanyNamedFn “SUPRA Ltd”) => 20 words away

Answer selector

• Will soon be using dependency analyzer to improve answer selection

Mary Jones works at ACME Inc.

works [A_nx0V] 1

args:

Mary_Jones [A_NXN] 0

args:

modifiers:

modifiers:

at [B_vxPnx] 2 args:

ACME_Inc. [A_NXN] 3

args:

modifiers:

modifiers:

Semantic Knowledge Source Integration (SKSI)

• Declarative description of knowledge source via Knowledge Base edits.

• Declarative description of access path via Knowledge Base edits

• On-line integration with Cyc’s Inference Engine

Backchaining into NIMA database QUERY:

(locatedAtPoint-SurfaceGeographical

(PlaceNamedFn "Faedis") (LatLongFn ?LAT ?LONG ))

Declarative description of knowledge source via KB edits:

(implies (NimaGnsPred ?PLACE ?LAT ?LONG)

(locatedAtPoint-SurfaceGeographical

?PLACE (LatLongFn ?LAT ?LONG)))

Converting CycL into NIMA SQL query

(NimaGnsPred (PlaceNamedFn “Faedis”) ?LAT ?LONG)

"SELECT dd_lat,dd_long FROM gns WHERE (full_name_nd = 'Faedis')”

dd_lat | dd_long ----------+--------- 46.1533 | 13.3431(1 row)

(?LAT . 46.15330123901367d0) (?LONG . 13.343099594116211d0)

NIMA integration example query

Query:

(and

(placeOfDeath AntoninArtaud ?PLACE)

(locatedAtPoint-SurfaceGeographical ?PLACE (LatLongFn ?LAT ?LONG )))

Answer:(?PLACE . CityOfIvry-sur-SeineFrance)

(?LAT . 48.81669998168945d0)

(?LONG . 2.3833301067352295d0)

QUIRK’s Blackboard: CyBlack

• CycL-to-GuruQA translator

• Paragraph retriever

• Answer Selector

• Inference Engine

are all connected to the Cyc Blackboard

more agents can be added without disturbing

the existing architecture

Use of external oraclesExamples from www.xmethods.org

• As alternative source of textual information– Search engines (e.g. Google)

– U.S. Code of Federal Regulations

– Searches SEC's EDGAR database

• As providers of services– Obtain list of ATM machines from ZIP codes

– Find under whose name a telephone number is registered

– Find out about current weather conditions in a given geographical region

– Validate Street Address

• Generally: code support for inference via Web Services

Example:Google client

• Implements – (webCorpusTermDocumentCount TERM ?COUNT)

• Potential for declarative representation in an ontology of expert sources

• Can be used to– improve generation of GuruQA queries

– order search by approximating relative importance/obscurity of terms

(and (isa ?WHO GermanPerson) (isa ?WHO Musician))

Frankenstein: 0.5M Mozart: 1.5M

KarlMarx: 290K Sting:1.25M

Beethoven:113K Beethoven: 113K

… …

… …

… …

Harry Tasso:92 KeithGodchaux:1280

Query interpretation tools

• We have integrated from other Cycorp projects:

– Question parser with disambiguation dialog

– Scenario constructor (for queries that would be too complex to express in a Natural Language)

Question parser and clarification dialog

• Which paintings about war did Picasso create?

Question parser and clarification dialog

SQ1

• SQ1

SQ2

SQ3

Scenario constructor