Text Understanding Agents and the Semantic Web Akshay Java, Tim Finin, Sergei Nirenburg 01/04/2005

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

WWW Motivation Intelligent agents need knowledge and information. Most Web content is NL text. SW can benefit NLP tools in their language understanding tasks Web of documents Web of data Text Images Audio video Ontologies Instances triples Natural Language RDF/OWL Facts from NL structured information Semantic Web NLP Tools

Motivation Language Understanding Agents Provides RDF version of the news.

Ontological Semantics OntoSem 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 Semantics

Static Knowledge Sources Ontology 8000 concepts Avg 16 properties each Lexicons English: entries Spanish: entries Chinese: 3000 entries Fact repository facts Onomasticon NNNNN names

The OntoSem Ontology PROPERTY FILLER FACET ONTOLOGY ::= CONCEPT+ CONCEPT ::= ROOT | OBJECT-OR-EVENT | PROPERTY SLOT ::= PROPERTY | FACET | FILLER

Text Meaning Representation (TMR) Word sense addressed disambiguated A persistent fact stored in the FR Semantic dependency established

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-NAMEColin 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 Text Meaning Representation (TMR) He asked the UN to authorize the war.

Mapping OntoSem to web based KR NL Text OntoSem OWL Ontology Lexicon OntoSem2OWL Fact Repository TMR Ontology TMRs In OWL

Mapping Rules for Classes OntoSem 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: he exclusive right to make, use or sell an invention, which is granted to the inventor

Mapping 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… (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… "A relation which relates concepts to what they can control" (make-frame (domain (range (is-a (inverse

Mapping Rules for Facets Facets 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

Evaluation Swoop Pellet Wonderweb Built Ontology translation tool using Jena API Total Triples Generated ~ (including bnode) Time to build the Model ~ sec Time to do RDFS Inference ~ 10 sec Time to do OWL Micro ~ 40 sec Time to do OWL Full ~ ???? DL Expressivity: ELUIH EL - Conjunction and Full Existential Quantification U - Union H - Role Hierarchy I - 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

Evaluation 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.

An Application Testbed: SemNews Semantically Search and Browse news Aggregators collect the RSS news descriptions form various sources. The sentences are processed by OntoSem and are converted into TMRs Provides intelligent agents with the latest news in a machine readable format

Semantic RSS Data Aggregators News Feeds OntoSem TMRs FR Language Processing OntoSem2OWL Dekade 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 RSS Aggregator

Agent understandable news Provides RDF version of the news.

Semantacizing RSS View structured representation of the RSS news story. Future versions would enable editing the facts and provide provenance information

News stories are ontologically linked Find news stories by browsing through the OntoSem ontology.

Tracking Named Entities Find stories on a specific named entity.

Browsing Facts Fact 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

Querying the semanticized RSS RDQL Queries Provides structured querying over text repre- sented in RDF.

Semantic Alerts Alerts can be specified as ontological concepts/ keywords / RDQL queries. Subscribe to results of structured queries

Beyond keyword search Conceptually searching for content Find all news stories that have something to do with a place and a terrorist activity. Context based querying Find all events in which ‘George Bush’ was the ‘speaker’. Reporting facts Find all politicians who traveled to Asia. Knowledge sharing Populating instances by mapping FOAF and DC to OntoSem ontology.

Current work Enron corpus Profiles in terror

Conclusions 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.

For More Information Semnews application OntoSem NLP system UMBC ebiquity research group This presentation

References Software Used [1] OntoSem [2] RDF Validation service [3] Jena Toolkit [4] Swoop Ontology Viewer [5] Pellet OWL DL Reasoner [6] Wonder Web 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 [2] pellet users [3] Semantic web [4] W3c RDF Interest [5] W3c Semantic web

Backup slides

Reasoning Capabilities Buildfile: build.xml init: compile: dist: [jar] Building jar: /home/aks1/software/eclipse/workspace/ontojena/dist/lib/ontojena.jar run: [java] MODEL OK [java] Resource: [java] - ( rdfs:subClassOf [java] - ( rdfs:subClassOf [java] - ( rdfs:subClassOf [java] - ( rdfs:subClassOf [java] - ( rdfs:subClassOf [java] - ( rdfs:subClassOf [java] - ( rdfs:subClassOf [java] - ( rdfs:subClassOf [java] - ( rdfs:subClassOf [java] - ( rdfs:subClassOf [java] - ( rdfs:subClassOf [java] - ( rdfs:subClassOf [java] - ( rdfs:label ' "a truck with equipment for fighting fires"') [java] - ( rdf:type owl:Class) [java] fire-engine recognized as subclas of vehicle BUILD SUCCESSFUL Total time: 10 seconds real 0m11.144s user 0m9.530s sys 0m0.190s ontojena]$ Finding Transitive Closures (RDFS reasoning) Fire-engine Truck Wheeled-engine-vehicle Engine-propelled--vehicleWheeled--vehicle Land-vehicle vehicle Inferred Triples

Mapping Rules CaseFrequencyMapped Using 1domain617rdfs:domain 2domain with not facet16owl:disjointWith 3range406rdfs:range 4range with not facet5owl:disjointWith 5inverse260owl:inverseOf Property Related Constructs

Mapping Rules CaseFrequencyMapped Using 1value18217owl:Restriction 2sem5686owl:Restriction 3relaxable-to95annotation 4default350Not handled 5default-measure612Not handled 6not134owl:disjointWith 7inv1941Not required Facet related constructs