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

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QALL-ME: Ontology and Semantic Web

  1. 1. Co-funded by the European Union QALL-ME: Ontology andQALL-ME: Ontology and Semantic WebSemantic Web Constantin Orasan University of Wolverhampton http://clg.wlv.ac.uk
  2. 2. Structure of presentation 1. The QALL-ME ontology 2. The ontology for answer retrieval 3. The ontology for bibliographical domain 4. The ontology for presentation 5. Where next?
  3. 3. Author, Title - Date 3 Ontology in QALL-MEOntology 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
  4. 4. 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)
  5. 5. 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
  6. 6. Part of the ontology (cinema/movies)
  7. 7. 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
  8. 8. Semantic annotation and database organization XML Schema XML Documents RDF Documents Define DetermineDetermine Transform QALL-ME Ontology HTML Parser Download World Wide Web Convert Database Convert
  9. 9. Ontology for answer retrieval
  10. 10. 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>
  11. 11. 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> }
  12. 12. Ontology for MRP  Minimal Relation Patterns represent relations in the ontology  Can be used in text entailment  Already presented
  13. 13. 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
  14. 14. 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).
  15. 15. 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>?
  16. 16. 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 Baseline Semantic engine English 42.46% 65% German 34.96% 64.88%
  17. 17. How do we move to another domain?
  18. 18. 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
  19. 19.  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
  20. 20. How do we interact with the user?
  21. 21.  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
  22. 22.  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)
  23. 23.  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
  24. 24. PREFIX declarations CONSTRUCT { results triples AnswersObject triples QuestionInterpretation triples } WHERE { OPTIONAL { selection triples } . }
  25. 25. qmq:qi rdf:type qmq:QuestionInterpretation; qmq:hasInterpretation "In which cinema is [MOVIE] showed on [TIME]" ; 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”.
  26. 26. Faceted browsing
  27. 27. 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
  28. 28. Thank you !

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