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QALL-ME: Ontology and Semantic Web

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Invited talk at Driving Future Question Answering: Research Trends And Market Perspectives Workshop, Trento, Italy

Invited talk at Driving Future Question Answering: Research Trends And Market Perspectives Workshop, Trento, Italy

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    QALL-ME: Ontology and Semantic Web QALL-ME: Ontology and Semantic Web Presentation Transcript

    • QALL-ME: Ontology and Semantic Web Constantin Orasan University of Wolverhampton http://clg.wlv.ac.uk
    • Structure of presentation
      • The QALL-ME ontology
      • The ontology for answer retrieval
      • The ontology for bibliographical domain
      • The ontology for presentation
      • Where next?
    • Ontology in QALL-ME
      • The QALL-ME ontology provides a conceptualised description of the domain in which the system is used
      • It is used to:
        • Provide a bridge between languages
        • Pass information between different components of the system
        • Encode the data
        • Retrieve the data
      Author, Title - Date
    • QALL-ME ontology
      • An ontology for the domain of tourism was developed and used in the prototype (Ou et. al., 2008)
      • Experiments with (existing) ontologies for the bibliographical domain were carried out (Orasan et. al., 2009)
    • Ontology for the domain of tourism
      • Developed to address the user needs
      • Inspired by existing ontologies such as Harmonise, eTourism, etc.
      • … but developed specially for the project
      • Aligned it to WordNet and SUMO
      • Freely available from the QALL-ME website
    • Part of the ontology (cinema/movies)
    • Semantic annotation and database organization
      • The ontology was used to encode the data
      • Annotated data from the content providers was converted to RDF triplets
      • The RDF documents can be stored in databases or plain text files
      • The Jena RDF API was used for the operations
    • Semantic annotation and database organization XML Schema XML Documents RDF Documents Define Determine Determine Transform QALL-ME Ontology HTML Parser Download World Wide Web Convert Database Convert
    • Ontology for answer retrieval
      • What movie starring Halle Berry is on in Birmingham?
      • Class: MovieShow 
      • Property: isInSite , Range: Cinema 
      • Property: hasPostalAddress , Range: PostalAddress 
      • Property: isInDestination , Range: Destination
      • Property: name , Range: string < Birmingham >
      • Property: hasEventContent , Range: Movie 
      • Property: name , Range: string < unknown >
      • Property: hasStar , Range: Star 
      • Property: name , Range: string < Halle Berry >
      • PREFIX qme: http://qallme.itc.it/ontology/qallme-tourism.owl#
      • PREFIX xsd: http://www.w3.org/2001/XMLSchema#
      • SELECT ?movieName
      • WHERE {
        • ?MovieShow qme:isInSite ?Cinema.
        • ?Cinema qme:hasPostalAddress ?PostalAddress.
        • ?PostalAddress qme:isInDestination ?Destination.
        • ?Destination qme:name “ Birmingham ”^^<xsd:string>
        • ?MovieShow qme:hasEventContent ?Movie.
        • ?Movie qme:name ?movieName.
        • ?Movie qme:hasStar ?Star.
        • ?Star qme:name “ Halle Berry ”^^<xsd:string>
      • }
    • Ontology for MRP
      • Minimal Relation Patterns represent relations in the ontology
      • Can be used in text entailment
      • Already presented
    • Ontology for generation of hypothesis
      • Starting from the ontology we can create hypothesis
        • What is the name of the movie with [DIRECTOR]?
        • What is the director of the movie with the name [NAME]?
      • Can be done for any language
      • Can generate the SPARQL at the same time
      • Can be done for any domain
    • Ontology generated patterns
      • 91% of the questions from the benchmark have one or two constrains
      • Investigation of the benchmark indicated three types of questions:
        • T1 – Query the name of a site or event which has one or more non-name attributes; Can you tell me the name of a Chinese restaurant in Walsall?
        • T2 – Query a non-name attribute of a site or event whose name is known; and Can you give me the address for the Kinnaree Thai Restaurant?
        • T3 – Query a non-name attribute of a site or event whose name is unknown but using its other non-name attribute(s) as the constraint(s).
      • Could you give me a contact number for an Italian restaurant in Solihull?”
      • can be decomposed into the following two questions:
      • T1: could you give me the name of an Italian restaurant in Solihull?
      • T2: could you give me a contact number for <the name of the restaurant in T1>?
    • Automatically generated patterns
      • the ontology can be used to generate patterns for T1 and T2 questions with one or two constraints
      • 2703 patterns were generated for English and German
      • generated also the SPARQLs
      • Evaluation on 200 questions
      • Baseline = cosine bag of words
      • Semantic engine = similarity on concepts + EAT + entity filtering
      • Language and domain independent
      64.88% 34.96% German 65% 42.46% English Semantic engine Baseline
    • How do we move to another domain?
    • Domain of scientific publications
      • Experiments for the bibliographic domain were carried out What papers did C. Orasan published in 2008?
      • Existing ontologies were combined:
        • Semantic Web for Research Communities (SWRC) models concepts from the research community
        • A subset of Dublin Core was used to describe the properties of a bibliographical entry
        • Simple Knowledge Organisation System (SKOS) was used to model relations between terms
      • The data from BibTeX format was converted to the domain ontology
      • SPARQL patterns were generated
      • The retrieval algorithm was not changed
      • … but some changes had to be introduced at the level of framework
    • How do we interact with the user?
      • User satisfaction is largely determined by aspects such as the ease of use, learning curve, feedback, interface friendliness, etc. and not just by accuracy.
      • What movies can I see at Symphony Hall this week?
      • If no answers:
        • Look for a different location
        • Search for a different time period
        • Wrong presupposition
        • User preferences
      • Most of the Feedback desiderata can be met without changing the current pipeline.
        • 'understanding' occurs in the Entailment engine (EE)
        • the QPlanner does not have direct access to this information, but
        • it can be injected in the results via the generated SPARQL, exploiting the RDF data model
      • Interactive Question Answering (IQA) ontology (Magnini et. al., 2009)
      • A question is analysed in terms of:
        • Expected answer type
        • Constraints
        • Context
      • The answer will contain:
        • Core Information
        • Justification
        • Complementary information
      • The situation can be handled using a rich SPARQL
      • Rewriting rules for the SPARQL in case of empty answer
    • PREFIX declarations CONSTRUCT { results triples AnswersObject triples QuestionInterpretation triples } WHERE { OPTIONAL { selection triples } . }
    • qmq:qi rdf:type qmq:QuestionInterpretation; qmq:hasInterpretation &quot;In which cinema is [MOVIE] showed on [TIME]&quot; ; qmq:hasConstraint qmq:c1; qmq:hasConstraint qmq:c2; qmq:hasFacet qmq:f1. qmq:c2 rdf:type qmq:Filter; qmq:hasType qmo:DatePeriod; qmq:hasProperty qmo:startDate; qmq:hasValue '''[TIMEX2]''' ; qmq:failureReason “ No film can be for the given date”.
    • Faceted browsing
    • Where next?
      • We have the technology to “convert” a natural language question to SPARQL, via an ontology
      • We can get access to a large number of resources using Linked Open Data
      • We can expand the access to knowledge
    •  
    • Thank you !