NLDB 2004

ORAKEL: A Natural Language Interface to an F-Logic Knowledge

Base

Philipp Cimiano

Institute AIFB

University of Karlsruhe

NLDB 2004

NLDB 2004

Outline Aim & Scope System Architecture F-Logic in a Nutshell Semantic Construction Component Lexicon Generation Component Conclusion & Further Work

NLDB 2004

Aims & Scope Aim: accessing a KB via natural

language translating language into logical queries

Who owns a company? Who owns every company? Who does not own a company?

Scope: consider only factoid questions simple syntactic structure: SVO +PP?

NLDB 2004

System Architecture

General Lexicon

Domain Lexicon

Lexicon Generation

Parsing + Sem.Construction

NL query

answer

F-Logic KBOntobroker

logicalquery

NLDB 2004

F-Logic in A Nutshell frames/methods:

microsoft[name ->„Microsoft“;

boss -> bill_gates; revenue(2002) -> 28.370.000.000]

subclasses: company::organization class-membership: microsoft:company queries: ][:? microsoftownXbossX

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Semantic Construction compositional semantics approach to

map NL questions to F-Logic queries relies on LTAG-style parsing

formalism developed in [Muskens 01] extension to accommodate

ontological concepts [Cimiano & Reyle 03]

NLDB 2004

Who owns Microsoft?

)(:.

:

1

1

XQpersonXQ

personwh

wh

Who

NLDB 2004

Who owns Microsoft?

)(:.

:

1

1

XQpersonXQ

personwh

wh

Who

)(21

1

swhs i

s

12

2

vps

s

},{ bossbosswh CCi

1dp1vp

))((.12

2

ZZ

vp

vdpvp

][.. YownXYX

v

v companydp 1

ie

owns

NLDB 2004

Who owns Microsoft?)(

21

1

swhs i

s

12

2

vps

s

},{ bossbosswh CCi

1dp1vp

))((.12

2

ZZ

vp

vdpvp

][.. YownXYX

v

v companydp 1

ie

owns

)(:.

:

1

1

XQpersonXQ

personwh

wh

Who

)(.

:

2

2

microsoftPP

companydp

dp

Microsoft

NLDB 2004

Who owns Microsoft?

)(:.

:

1

1

XQpersonXQ

personwh

wh

Who

)(21

1

swhs i

s

12

2

vps

s

},{ bossbosswh CCi

1dp1vp

))((.12

2

ZZ

vp

vdpvp

][.. YownXYX

v

v companydp 1

ie

owns )(.

:

2

2

microsoftPP

companydp

dp

Microsoft

43?3

ss

s

4s ?

NLDB 2004

Who owns Microsoft?)(

21

1

swhs i

s

12

2

vps

s

},{ bossbosswh CCi

1dp1vp

))((.12

2

ZZ

vp

vdpvp

][.. YownXYX

v

v companydp 1

ie

owns

)(:.

:

1

1

XQpersonXQ

personwh

wh

Who

)(.

:

2

2

microsoftPP

companydp

dp

Microsoft

43?3

ss

s

4s ?

NLDB 2004

Who owns Microsoft?

)(:.

:

1

1

XQpersonXQ

personwh

wh

Whov

43?3

ss

s

4s ?

)(21

1

swhs i

s

12

2

vps

s

},{ bossbosswh CCi

1dp1vp

][.2

2

microsoftownZZ

vp

vp

ie

owns

companydp :2

Microsoft

NLDB 2004

Who owns Microsoft?

43?3

ss

s

4s ?

][:1

microsoftownXpersonX

s

2s

1dp 1vp

companydp :2

personwh :1

v

Who

owns Microsoft

ei

NLDB 2004

Who owns Microsoft?

1s

2s

1dp 1vp

companydp :2

personwh :1

v

Who

owns Microsoft

ei

][:?3

microsoftownXpersonX

s

?

NLDB 2004

Lexicon Generation (part I) person[own->company]

use corpus (BNC) parse it (LoPar) extract with tgrep

S V O (transitive) S V PP (intransitive + PP) S V O PP (transitive + PP) N PP N PP PP

find most appropriate synset for each argument w.r.t WordNet [Resnik 97]

NLDB 2004

Lexicon Generation (part II)

person[own -> company]

own:transitive: (45.90%)

subj: 100001740obj: 100001740

instransitive + PP: (4.10%)subj: 100001740PP(by): 100017954

take the one maximizing:

repeat the whole process with synonyms of most frequent synset, i.e. have and possess

)|())(),((

1

),(

vsPjcic v

ji WN

NLDB 2004

Lexicon Generation (part III)

person[own->company] own(subj: person, obj: company)

Generate elementary trees: Who owns Microsoft? Which company does Bill Gates own? Which company is owned by Bill

Gates?

NLDB 2004

Lexicon Generation (part IV)

)(.

:

2

2

microsoftPP

companydp

dp

Microsoft

Microsoft:company

)(.

:

2

2

xcompanyx

companynp

dp

company

NLDB 2004

Evaluation 5 ontologies from the DAML library:

beer wine personal information general information about organizations university activities

acquire an appropriate subcategorization frame for the binary relations

NLDB 2004

Results

Ontology

#Props

Dom.+Range

Non-Composite

Correct

%

Beer 9 4 3 2 66.67

Wine 10 10 9 6 44.44

Personal 25 10 8 6 75

General 28 17 6 6 100

University

27 11 2 1 50

Total 99 42 28 21 75

NLDB 2004

ConclusionORAKEL: translating NL questions into logical form theoretical (parsing) framework lexicon generation

differs from: AQUALOG (map triples to an ontology) FrameNet (mapping lexical/semantic

representations) AID (use TFIDF-based similarity) Schema-based (map words to database columns)

NLDB 2004

Further WorkFurther applications:

NL generation from an ontology Mapping from syntax to ontological

structures (MOSES)

Further Work: user feedback answer formulation real life evaluation application to construct the KB

NLDB 2004

EKAW 2004 Workshop on the Application of Language and Semantic

Technologies to support Knowledge Management

Submission deadline: 4. July!!!

Topics:

Multi-lingual systems, Information Extraction, Ontology Learning, Document Indexing, Retrieval and Browsing, Approaches to Semantic Annotation, Smart Browsing, Semantic Search, Question Answering, Enterprise Content Management

Organizing Committee:

Philipp Cimiano, AIFB, University of Karlsruhe, Germany. Fabio Ciravegna, Natural Language Processing Group, University of Sheffield, UK Enrico Motta, Knowledge Media Institute, The Open University, UK. Victoria Uren, Knowledge Media Institute, The Open University, UK.