Co-funded by the European Union

QALL-ME: Ontology and QALL-ME: Ontology and Semantic WebSemantic Web

Constantin Orasan

University of Wolverhampton

http://clg.wlv.ac.uk

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?

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

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)

MovieShow

Cinema

Movie

TicketPrice

DateTimePeriod

synposis

isInSitehasPrice

hasEventContent

hasPeriod

priceType

priceValue

Director

Star

Producer

Writer

Currency

GPSCoordinate

DirectionLocation

Contact

hasCurrency

TimePeriod

DatePeriod

startTimeendTime

endDate startDate

hasTimePeriod

hasDatePeriod

DirectionLocation

hasSiteFacility

hasContact

hasWriter

hasDirector

hasProducer

genre

name

hasPostalAddress

hasGPSCoordinate

PostalAddress

CinemaRoom

hasRoom

hasStar

certificate

SitePrice

Event

EventContentPeriod

subClassOfsubClassOf

subClassOf

subClassOfsubClassOf

SiteFacility

RoomFacility

hasRoomFacility

name description

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

DetermineDetermine

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 ?movieNameWHERE {

?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; andCan 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

Baseline Semantic engine

English 42.46% 65%

German 34.96% 64.88%

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 declarationsCONSTRUCT {

results triplesAnswersObject triplesQuestionInterpretation triples

}WHERE { OPTIONAL {

selection triples} . }

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”.

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 !