Shiyan Ou1, Viktor Pekar1, Constantin Orasan1, - PowerPoint PPT Presentation

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Shiyan Ou1, Viktor Pekar1, Constantin Orasan1,

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What movie starring Halle Berry is on in Birmingham? Class: MovieShow ... Property:name, Range: string Halle Berry 16 ... qme:name 'Halle Berry'^^ xsd:string ... – PowerPoint PPT presentation

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Title: Shiyan Ou1, Viktor Pekar1, Constantin Orasan1,


1
Development and Alignment of a Domain-Specific
Ontology for Question Answering
  • Shiyan Ou1, Viktor Pekar1, Constantin Orasan1,
  • Christian Spurk2, Matteo Negri3
  • 1Research Group in Computational Linguistics,
    University of Wolverhampton, UK
  • 2German Research Centre for Artificial
    Intelligence GmbH (DFKI), Germany
  • 3Fondazione Bruno Kessler FBK, Italy

2
Structure
  • Introduction to QALL-ME
  • The QALL-ME ontology
  • Alignment to WordNet and SUMO
  • How the ontology is used for data encoding
  • Conclusions

3
Introduction to QALL-ME
  • QALL-ME (Question Answering Learning technologies
    Multilingual Multimodal Environment) is an
    EU-funded project which aims to establish a
    shared infrastructure for multilingual and
    multimodal question answering in the domain
    tourism.
  • Projects website http//qallme.fbk.eu/
  • In the QALL-ME system
  • users pose natural language questions in several
    languages (both in textual and speech modality)
    using a variety of input devices (e.g. mobile
    phones), and
  • returns a list of specific answers formatted in
    the most appropriate modality, ranging from small
    texts, maps, videos, and pictures.
  • A domain-specific ontology for the tourism domain
    was developed and shared among all the partners.

4
The ontology in the project
WP3 Multilingual question interpretation
WP4 Annotation of entities Indexing of
data Retrieval of data
QALL-ME ontology
WP5 Multilingual answer extraction
WP9 Evaluation
See more in O39 Multilingual Resources
(Ambasadeurs) at 1305
5
Design of the ontology
  • Analysis of data from content providers
  • Analysis of users requirements
  • Inspired by similar ontologies such as Harmonise,
    eTourism, Hi-Touch, TAGA, GETESS
  • Harmonise and eTourism focus on static
    information (e.g. accommodation and
    events/activities), rather than dynamic
    information related to travel business (e.g.
    customers and itineraries) as the TAGA and
    Hi-Touch ontologies do.
  • Similar to eTourism as is written in OWL rather
    RDFs
  • but wider coverage than each individual existing
    ontology
  • Introspection

6
Technical details of the ontology
  • Encoded using OWL DL, since it has more
    expressive power than OWL Lite and has more
    efficient reasoning support than OWL Full
  • Used Protégé-OWL as the editor and RacerPro7 as
    the reasoner
  • The ontology contains
  • 122 classes (concepts),
  • 55 datatype properties and
  • 52 object properties which indicate the
    relationships among the 122 classes.
  • 15 top-level classes.
  • The class hierarchy has a maximum depth of 4.

7
Part of the ontology (cinema/movies)
8
Ontology alignment
  • The QALL-ME ontology was designed as a model of
    the narrow knowledge domain of tourism.
  • The QALL-ME ontology was complemented with
    information from WordNet (and implicitly
    MultiWordNet) and SUMO via alignment
  • The QALL-ME ontology is being changed so fully
    manual alignment was not a solution
  • Fully automatic alignment is not precise enough,
    but maybe semi-automatic alignment is a solution

9
Ontology alignment (II)
  • The alignment relied on
  • String similarity of element identifiers (e.g.
    chalet ? chalet_1, SiteFacilityForChildren ?
    facility_)
  • Structural similarity for disambiguation (e.g.
    uses the semantic distance to aligned concepts)
  • Definition similarity for disambiguation
    (similarity between comments in the ontology and
    WordNet glosses is used)
  • Structural similarity for unmatched concepts is
    calculated to all the nouns in WordNet

10
Ontology alignment (III)
  • The overall accuracy of the fully automatic
    alignment is clearly suboptimal (precision of 49
    and recall of 31),
  • Error analysis
  • We noticed that for concept names with
    unambiguous matches in WordNet the algorithm
    performs without any errors
  • The poor disambiguation performance is due to the
    very different depths of the two ontologies
  • Only a few concepts have comments which are
    useful for definition similarity
  • Semi-automatic alignment requires under 30
    minutes to obtain perfect alignment

11
Example of alignment
  • QALL-ME SUMO WN2.1 WN2.1 gloss
  • Accommodation _at_inhabits 02647858 living
    quarters provided for
  • public convenience
  • "overnight accommodations
  • are available"
  • Chalet _at_Building 02973228 a Swiss house with
    a sloping
  • roof and wide eaves or a
  • house built in this style
  • PostOffice _at_Organization 08034771 an
    independent agency of the
  • federal government
  • responsible for mail delivery

12
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

13
Semantic annotation and database organization
14
Content retrieval
  • For retrieval SPARQL is used
  • SPARQL is a query language for accessing RDF
    graphs by the W3C RDF Data Access Working Group
  • SPARQL provides interoperability between languages

15
  • 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 ltBirminghamgt
  • Property hasEventContent, Range Movie ?
  • Property name, Range string ltunknowngt
  • Property hasStar, Range Star ?
  • Propertyname, Range string ltHalle Berrygt

16
  • PREFIX qme http//qallme.itc.it/ontology/qallme-t
    ourism.owl
  • PREFIX xsd http//www.w3.org/2001/XMLSchema
  • SELECT ?movieName
  • WHERE
  • ?MovieShow qmeisInSite ?Cinema.
  • ?Cinema qmehasPostalAddress ?PostalAddress.
  • ?PostalAddress qmeisInDestination ?Destination.
  • ?Destination qmename Birminghamltxsdstringgt
  • ?MovieShow qmehasEventContent ?Movie.
  • ?Movie qmename ?movieName.
  • ?Movie qmehasStar ?Star.
  • ?Star qmename Halle Berryltxsdstringgt

17
Conclusions
  • The QALL-ME ontology was specifically designed
    for the domain of tourism
  • The ontology is playing an important role in
    several parts of the project
  • The current ontology went through several
    revisions before reaching the current stage (and
    it may change again!!!)
  • Both the ontology and its alignment to WordNet
    and SUMO will be made freely available on the
    projects website

18
Thank you !
Projects website http//qallme.fbk.eu/
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