Integrating Language Understanding agents

into the Semantic Web

Integrating Language Understanding agents

into the Semantic Web

Akshay Java, Tim Finin, Sergei Nirenburg 11/04/2005 Akshay Java, Tim Finin, Sergei Nirenburg 11/04/2005

OutlineOutline

• Motivation: Language Understanding Agents• Ontological Semantics• Bridging the Knowledge Gap• Preliminary Evaluation• SemNews: An Application Testbed• Conclusion• Q&A

WWWWWW

MotivationMotivation• Intelligent agents need knowledge and information.• Majority of content on the web remains in NL text.• SW can benefit NLP tools in their language

understanding task

Web of documents Web of data

Text

Images

Audio

video

Ontologies

Instances

triples

Natural Language RDF/OWL

Facts from NL

structured information

Semantic Web

NL

P T

ools

MotivationMotivation

Language Understanding Agents

Provides RDF version of the news.

Ontological SemanticsOntological SemanticsOntoSem is a Natural Language Processing System that processes the text and converts them into facts.

Supported by a constructed world model encoded in a rich Ontology.

Ontological SemanticsOntological Semantics

InputText

Text MeaningRepresentation(TMR)

Grammar:Ecology

MorphologySyntax

Lexicon andOnomasticon

Static Knowledge Resources

Ontology andFact Repository

Preprocessor

Syntactic

Analyzer

Semantic

Analyzer

Mapping OntoSem to web based KR

Mapping OntoSem to web based KR

• OntoSem ontology is a frame based representation

ONTOLOGY ::= CONCEPT+CONCEPT ::= ROOT | OBJECT-OR-EVENT | PROPERTYSLOT ::= PROPERTY | FACET | FILLER

• Translating OntoSem Ontology deals with mapping its semantics into corresponding OWL representation.

• OntoSem’s supporting fact repositories are also mapped to OWL.

• The text meaning representation of the sentences is now converted to OWL.

Mapping OntoSem to web based KR

Mapping OntoSem to web based KR

NL Text OntoSem

OWLOntology

Lexicon OntoSem2OWL

FactRepository

TMR

Ontology

TMRsIn OWL

Mapping Rules for ClassesMapping Rules for ClassesOntoSem LISP version

(make-frame patent

(definition (value (common "the exclusive right to make, use or sell an invention, which is granted to the inventor")))

(is-a (value (common intangible-asset legal-right))))

OWL Version:

• <owl:Class rdf:about="&ontosem;patent">• <rdfs:subClassOf>• <owl:Class rdf:about="&ontosem;intangible-asset">• </owl:Class>• </rdfs:subClassOf>• <rdfs:subClassOf>• <owl:Class rdf:about="&ontosem;legal-right">• </owl:Class>• </rdfs:subClassOf>• <rdfs:comment>he exclusive right to make, use or • sell an invention, which is granted to the inventor• </rdfs:label>• </owl:Class>

Mapping Rules for PropertiesMapping Rules for Properties

• Properties can be• ObjectProperty owl:ObjectProperty• Datatype Property owl:DatatypeProperty

• Property hierarchy is defined by owl:subPropertyOf

• Domain maps to rdfs:domain• Range maps to rdfs:range• Restrictions are handled using owl:Restriction• Numeric datatypes are handled using XSD

Mapping Rules for Properties…Mapping Rules for Properties…(make-frame controls

(domain (sem (common physical-event physical-object

social-event social-role))) (range

(sem (common actualize artifact natural-object social-role)))

(is-a (value (common relation))) (inverse (value (common controlled-by))) (definition

(value (common "A relation which relates concepts to what

they can control"))))

Mapping Rules for Properties…Mapping Rules for Properties…<owl:ObjectProperty rdf:ID= "controls">

<rdfs:domain> <owl:Class> <owl:unionOf rdf:parseType="Collection">

<owl:Class rdf:about="#physical-event"/> <owl:Class rdf:about="#physical-object"/><owl:Class rdf:about="#social-event"/>

<owl:Class rdf:about="#social-role"/> </owl:unionOf> </owl:Class>

</rdfs:domain> <rdfs:range>

<owl:Class> <owl:unionOf rdf:parseType="Collection">

<owl:Class rdf:about="#actualize"/> <owl:Class rdf:about="#artifact"/> <owl:Class rdf:about="#natural-object"/> <owl:Class rdf:about="#social-role"/>

</owl:unionOf> </owl:Class>

</rdfs:range> <rdfs:subPropertyOf>

<owl:ObjectProperty rdf:about="#relation"/> </rdfs:subPropertyOf> <owl:inverseOf rdf:resource="#controlled-by"/> <rdfs:label> "A relation which relates concepts to what they can control" </rdfs:label>

</owl:ObjectProperty>

(make-frame

(domain

(range

(is-a(inverse

Mapping Rules for FacetsMapping Rules for FacetsFacets are a way to restricting the fillers that can be used for a

particular slot

• SEM and VALUE• Maps them using owl:Restriction on a particular property.

• RELAXABLE-TO• Add this to the classes present in owl:Restriction and add this

information in the annotation.• DEFAULT

• No clear way to represent non-monotonic reasoning and closed world assumptions in Semantic Web.

• DEFAULT-MEASURE• similar to DEFAULT Facet, not handled.• DEFAULT, DEFAULT-MEASURE used relatively less frequently

• NOT• Not facet can be handled using owl:disjointOf

• INV• need not be handled since is-a slot is already mapped to owl:inverseOf

Mapping RulesMapping Rules

Case Frequency Mapped Using

1 domain 617 rdfs:domain

2 domain with not facet 16 owl:disjointWith

3 range 406 rdfs:range

4 range with not facet 5 owl:disjointWith

5 inverse 260 owl:inverseOf

Property Related Constructs

Mapping RulesMapping Rules

Case Frequency Mapped Using1 value 18217 owl:Restriction

2 sem 5686 owl:Restriction

3 relaxable-to 95 annotation

4 default 350 Not handled

5 default-measure 612 Not handled

6 not 134 owl:disjointWith

7 inv 1941 Not required

Facet related constructs

Translating TMR2OWLTranslating TMR2OWL

Translating TMRs involves instantiation of concepts mapped in OWL.

Example:(COME-1740

(TIME (VALUE (COMMON (FIND-ANCHOR-TIME))))

(DESTINATION(VALUE (COMMON CITY-1740)))

(AGENT (VALUE (COMMON POLITICIAN-1740))) (ROOT-WORDS (VALUE (COMMON (ARRIVE)))) (WORD-NUM (VALUE (COMMON 2))) (INSTANCE-OF (VALUE (COMMON COME)))

<ontosem:come rdf:about="COME-1740"> <ontosem:destination rdf:resource="#CITY-1740"/> <ontosem:agent rdf:resource="#POLITICIAN-1740"/>

</ontosem:come>

EvaluationEvaluation

http://w3c.org/RDF/Validator/

Swoop

Pellet

Wonderweb

Built Ontology translation tool using Jena API Total Triples Generated ~ 102189 (including bnode)

Time to build the Model ~ 10-40 sec

Time to do RDFS Inference ~ 10 sec

Time to do OWL Micro ~ 40 sec

Time to do OWL Full ~ ????

DL Expressivity: ELUIHEL - Conjunction and Full Existential QuantificationU - UnionH - Role HierarchyI - Role Inverse

Total Number of Classes: 7747 (Defined: 7747, Imported: 0)Total Number of Datatype Properties: 0 (Defined: 0, Imported: 0)Total Number of Object Properties: 604 (Defined: 604, Imported: 0)Total Number of Annotation Properties: 1 (Defined: 1, Imported: 0)Total Number of Individuals: 0 (Defined: 0, Imported: 0) NOTE: This is using no Restrictions

After Translation

OWL FULL

EvaluationEvaluation

• Syntactic Correctness: was checked using OWL/RDF validators.

• Semantic Validation: Full semantic validation even for subsets of OWL is difficult.

• Meaning Preservation: some subset of the native representation features such as DEFAULTS, modality, case roles may be underrepresented or not handled.

• Feature Minimization: Complex features could be difficult for reasoners to handle hence we can perform the translations at each of the levels – OWL Lite, OWL DL, OWL Full.

• Translation Complexity: OntoSem is an extensive and large ontology (~8000 concepts). Translation itself is done syntactically but in general translation might require reasoning which could be an issue.

Reasoning CapabilitiesReasoning CapabilitiesBuildfile: build.xml

init:

compile:

dist: [jar] Building jar: /home/aks1/software/eclipse/workspace/ontojena/dist/lib/ontojena.jar

run: [java] MODEL OK [java] Resource: http://ontosem.org/#fire-engine [java] - (http://ontosem.org/#fire-engine rdfs:subClassOf http://ontosem.org/#fire-engine) [java] - (http://ontosem.org/#fire-engine rdfs:subClassOf http://ontosem.org/#all) [java] - (http://ontosem.org/#fire-engine rdfs:subClassOf http://ontosem.org/#physical-object) [java] - (http://ontosem.org/#fire-engine rdfs:subClassOf http://ontosem.org/#inanimate) [java] - (http://ontosem.org/#fire-engine rdfs:subClassOf http://ontosem.org/#wheeled-vehicle) [java] - (http://ontosem.org/#fire-engine rdfs:subClassOf http://ontosem.org/#engine-propelled-vehicle) [java] - (http://ontosem.org/#fire-engine rdfs:subClassOf http://ontosem.org/#wheeled-engine-vehicle) [java] - (http://ontosem.org/#fire-engine rdfs:subClassOf http://ontosem.org/#artifact) [java] - (http://ontosem.org/#fire-engine rdfs:subClassOf http://ontosem.org/#object) [java] - (http://ontosem.org/#fire-engine rdfs:subClassOf http://ontosem.org/#land-vehicle) [java] - (http://ontosem.org/#fire-engine rdfs:subClassOf http://ontosem.org/#vehicle) [java] - (http://ontosem.org/#fire-engine rdfs:subClassOf http://ontosem.org/#truck) [java] - (http://ontosem.org/#fire-engine rdfs:label ' "a truck with equipment for fighting fires"') [java] - (http://ontosem.org/#fire-engine rdf:type owl:Class) [java] fire-engine recognized as subclas of vehicle

BUILD SUCCESSFULTotal time: 10 seconds

real 0m11.144suser 0m9.530ssys 0m0.190s[aks1@trishuli ontojena]$

Finding Transitive Closures (RDFS reasoning)

Fire-engine

Truck

Wheeled-engine-vehicle

Engine-propelled--vehicle Wheeled--vehicle

Land-vehicle

vehicleInferred Triples

An Application Testbed: SemNewsAn Application Testbed: SemNews

• SemNews: Semantically Search and Browser news

• Aggregators collect the RSS news descriptions form various sources.

• The sentences are processed by OntoSem and are converted into Text Meaning Representations (TMRs)

• Provides intelligent agents with the latest news in a machine readable format

http://semnews.umbc.edu

Semantic RSS

Data Aggregators

News Feeds

OntoSem

TMRs FR

Language Processing

OntoSem2OWLDekade Editor

Knowledge Editor Environment Semantic Web Tools

OntoSem Ontology (OWL)

TMR

Inferred

Triples

Fact Repository Interface

Ontology &Instance browser

Text Search

RDQL Query

Swoogle Index

1 2

56

7

8

9

3 4

10

11

12

13

14

15

RSSAggregator

http://semnews.umbc.edu

Agent understandable newsAgent understandable news

Provides RDF version of the news.

http://semnews.umbc.edu

Semantacizing RSSSemantacizing RSS

View structured representation of the RSS news story.

Future versions would enable editing the facts and provide provenance information

http://semnews.umbc.edu

News stories are ontologically linked

News stories are ontologically linked

Find news stories by browsing through the OntoSem ontology.

http://semnews.umbc.edu

Tracking Named EntitiesTracking Named Entities

Find stories about a specific named entity.

http://semnews.umbc.edu

Browsing FactsBrowsing FactsFact repository explorer for named entity ‘Mexico’ shows that it has a relation ‘nationality-of’ with CITIZEN-235

Fact repository explorer for instance CITIZEN-235 shows that the citizen is an agent of ESCAPE-EVENT

http://semnews.umbc.edu

Querying the semanticized RSSQuerying the semanticized RSS

RDQL Queries

Provides structured querying over text converted into RDF representation.

http://semnews.umbc.edu

Semantic AlertsSemantic Alerts

Alerts can be specified as ontological concepts/ keywords / RDQL queries.

Subscribe to results of structured queries

http://semnews.umbc.edu

ConclusionsConclusions

• Integrating language processing agents into the SW would publish SW annotations and documents that capture the text’s meaning.

• Migrating from native non-web based representation to SW representation may be loss-full but is still useful for many applications.

• SemNews application testbed demonstrates some scenarios that can benefit from language understanding agents.

Q&AThank you.

http://ebiquity.umbc.eduhttp://

semnews.umbc.edu

ReferencesReferencesSoftware Used[1] OntoSem http://ilit.umbc.edu/[2] RDF Validation service http://w3c.org/RDF/Validator[3] Jena Toolkit http://jena.sourceforge.net/[4] Swoop Ontology Viewer http://www.mindswap.org/2004/SWOOP/[5] Pellet OWL DL Reasoner http://www.mindswap.org/2003/pellet/[6] Wonder Web OWL Validator http://phoebus.cs.man.ac.uk:9999/OWL/Validator

Papers[1] Sergei Nirenburg and Victor Raskin, Ontological Semantics, Formal Ontology and Ambiguity[2] Sergei Nirenburg and Victor Raskin, Ontological Semantics, MIT Press, Forthcoming[3] Sergei Nirenburg, Ontological Semantics: Overview, Presentation CLSP JHU, Spring 2003[4] Marjorie McShane, Sergei Nirenburg, Stephen Beale, Margalit Zabludowski, The Cross Lingual Reuse and

Extension of knowledge Resources in Ontological Semantics[5] P.J Beltran-Ferruz, P.A Gonzalez-Calero, P. Gervas Converting Mikrokosmos frames into Description Logics.[6] Sergei Nirenburg, Ontology Tutorial, ILIT UMBC

Mailing Lists[1] Jena Developers jena-dev@yahoogroups.com[2] pellet users pellet-users@lists.mindswap.org[3] Semantic web semanticweb@yahoogroups.com[4] W3c RDF Interest www-rdf-interest@w3.org[5] W3c Semantic web semantic-web@w3.org

Backup slides

Static Knowledge SourcesStatic Knowledge Sources• Ontology 8000 concepts• Avg 16 properties each• English Lexicon 45000 entries• Spanish Lexicon 40000 entries• Chinese Lexicon 3000 entries• Fact repository 20000 facts

[Sergei Nirenburg, Ontological Semantics: Overview, Presentation CLSP JHU, Spring 2003]

Text Meaning Representation (TMR)

Text Meaning Representation (TMR)

He asked the UN to authorize the war.

REQUEST-ACTION-69   AGENT HUMAN-72 THEME ACCEPT-70   BENEFICIARY ORGANIZATION-71   SOURCE-ROOT-WORD ask TIME (< (FIND-ANCHOR-TIME))

ACCEPT-70   THEME WAR-73   THEME-OF REQUEST-ACTION-69   SOURCE-ROOT-WORD authorize

ORGANIZATION-71   HAS-NAME United-Nations  BENEFICIARY-OF REQUEST-ACTION-69   SOURCE-ROOT-WORD UN

HUMAN-72   HAS-NAME Colin Powell  AGENT-OF REQUEST-ACTION-69 SOURCE-ROOT-WORD he ; reference resolution has been carried out

WAR-73   THEME-OF ACCEPT-70   SOURCE-ROOT-WORD war

Example from[Marjorie McShane, Sergei Nirenburg, Stephen Beale, Margalit Zabludowski, The Cross Lingual Reuse and Extension of knowledge Resources in Ontological Semantics]

Text Meaning Representation (TMR)

Text Meaning Representation (TMR)

The OntoSem OntologyThe OntoSem Ontology

PROPERTY

FILLER

FA

CE

T