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

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. 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. 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. 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. 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. Part of the ontology (cinema/movies)

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. 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. Ontology for answer retrieval

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. 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. Ontology for MRP
 Minimal Relation Patterns represent relations
in the ontology
 Can be used in text entailment
 Already presented

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. 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. 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. 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. How do we move to another
domain?

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.  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. How do we interact with the
user?

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.  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.  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. PREFIX declarations
CONSTRUCT {
results triples
AnswersObject triples
QuestionInterpretation triples
}
WHERE { OPTIONAL {
selection triples
} . }

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. Faceted browsing

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

29. Thank you !