building an ontological base for experimental evaluation of semantic web applications
DESCRIPTION
Building an Ontological Base for Experimental Evaluation of Semantic Web Applications. Peter Bartalos, Michal Barla, Gyorgy Frivolt, Michal Tvarožek, Anton Andrejko, Mária Bieliková and Pavol Návrat {name.surname}@fiit.stuba.sk. Institute of Informatics and Software Engineering - PowerPoint PPT PresentationTRANSCRIPT
Building an Ontological Base for Experimental
Evaluation of Semantic Web Applications
Peter Bartalos, Michal Barla, Gyorgy Frivolt, Michal Tvarožek,
Anton Andrejko, Mária Bieliková and Pavol Návrat
{name.surname}@fiit.stuba.skInstitute of Informatics and Software Engineering
Faculty of Informatics and information Technologies Slovak University of Technology in Bratislava
Motivation
• Semantic Web applications Experimental Evaluation (SWEE)– Semantic annotation of the information
– Searching in semantic information space
• AKTORS
• Knowledge Web
• On-To-Knowledge
• NAZOU – job offers (nazou.fiit.stuba.sk) Tools for acquisition, organization and maintenance of knowledge in an
environment of heterogeneous information resources
• MAPEKUS – scientific publication (mapekus.fiit.stuba.sk)Modeling and Acquisition, Processing and Employing Knowledge About User Activities in the Internet Hyperspace
• Demand for well-built large scale ontologies with specific properties
• Filling the ontology with instances (not it’s creation) – building the A-box
Outline
• Approaches to ontological base creation
• Method for ontological test base building
• Evaluation
• Conclusions
Filling the ontology with instances
jo:JobOffer
jo:hasPrerequisite*
jo:offersPosition
jo:Organization
jo:isOfferedByjo:isOfferedVia
jo:hasBenefit*
jo:hasApplyInformation*
r:Region
jo:hasDutyLocation*
jo:Salary
jo:hasSalary
jo:offers*jo:mediates*
jo:isBasedAt
r:isPartOf* r:consistsOf*
jo:ApplyInformation
c:ProfessionClassification
jo:Prerequisite
jo:Benefit
Web
3) Wrappers
1) Generic editor
4) Generators
2) Specialized editor
SWEE OntologyFile HelpEdit
Profession
Clerks
Armed Forces
Lorem
Lorem
lor
Enter Text
Edit
lor
Lorem IpsimSaldksa;d saD sadSasadsa d’sadsaSalkdjlkfjd fld;skfDlks ljflkdsjfdsf kldsf dsdskjfldsk fj
Edit
Specialized editorSpecialized editor
jo:JobOffer
jo:hasPrerequisite*
jo:offersPosition
jo:Organization
jo:isOfferedByjo:isOfferedVia
jo:hasBenefit*
jo:hasApplyInformation*
r:Region
jo:hasDutyLocation*
jo:Salary
jo:hasSalary
jo:offers*jo:mediates*
jo:isBasedAt
r:isPartOf* r:consistsOf*
jo:ApplyInformation
c:ProfessionClassification
jo:Prerequisite
jo:Benefit
Generic editorGeneric editor
Profession
Clerks
Armed ForcesLorem Edit
Manual approaches
Automatic approaches
Generic ontology editors
• Understand the generic structure of the ontology
• Immediately usable
• Domain independent
• Insufficient validation and user comfort
• Suitable for experts (ontology engineers)
jo:JobOffer
jo:hasPrerequisite*
jo:offersPosition
jo:Organization
jo:isOfferedByjo:isOfferedVia
jo:hasBenefit*
jo:hasApplyInformation*
r:Region
jo:hasDutyLocation*
jo:Salary
jo:hasSalary
jo:offers*jo:mediates*
jo:isBasedAt
r:isPartOf* r:consistsOf*
jo:ApplyInformation
c:ProfessionClassification
jo:Prerequisite
jo:Benefit
Generic editorGeneric editor
Profession
Clerks
Armed ForcesLorem Edit
Generic ontology editors
Specialized ontology editors
• Freedom in adjusting to a given ontology and user requirements
• Sophisticated validation
based on the knowledge
of the ontology
• Development and maintenance costs
• Coupled to a ontology
• Suitable also for non-experts
File HelpEdit
Profession
Clerks
Armed Forces
Lorem
Lorem
lor
Enter Text
Edit
lor
Lorem IpsimSaldksa;d saD sadSasadsa d’sadsaSalkdjlkfjd fld;skfDlks ljflkdsjfdsf kldsf dsdskjfldsk fj
Edit
Specialized editorSpecialized editor
Specialized ontology editors
JOE – Job Offer Editor
Wrappers
• Parse Web pages and produce structured output
• Need well structured pages
• Do not need a human involvement
• Significant amount of acquired data
• Development and maintenance costs
Generators
• Reusing the already existing data
• Increase the size of the ontological base
• Instances of desired properties
• Development and maintenance costs
• Meaningfulness of the data
Approaches to Ontological Base Creation
• Different approaches have different benefits and disadvantages
• They support each other
• They can be adjusted
– Invested time
– Development of tools
Method of Ontological Base Creation
• Specification of the requirements for the ontology
– Amount of data
– Range of properties of the instances
– Instance detail
– Quality
• Analysis of the domain and information sources
• Generally no approach can separately satisfy the requirements
• Adjusting the manual and automatic approaches
Method of Ontological Base Creation
Web
) 3 Wrappers
SWEE Ontology
1jo:JobOffer
jo:hasPrerequisite*
jo:offersPosition
jo:Organization
jo:isOfferedByjo:isOfferedVia
jo:hasBenefit*
jo:hasApplyInformation*
r:Region
jo:hasDutyLocation*
jo:Salary
jo:hasSalary
jo:offers*jo:mediates*
jo:isBasedAt
r:isPartOf* r:consistsOf*
jo:ApplyInformation
c:ProfessionClassification
jo:Prerequisite
jo:Benefit
Generic editorGeneric editor
Profession
Clerks
Armed ForcesLorem Edit
) Generic editor
editor2) Specialized
File HelpEdit
Profession
Clerks
Armed Forces
Lorem
Lorem
lor
Enter Text
Edit
lor
Lorem IpsimSaldksa;d saD sadSasadsa d’sadsaSalkdjlkfjd fld;skfDlks ljflkdsjfdsf kldsf dsdskjfldsk fj
Edit
Specialized editorSpecialized editor 4) Generators
Satisfaction of the requirements to ontological data
Amount Range ofproperties
Details Resamblence toreal data
Sat
isfa
ctio
n o
f re
qu
irem
ents
Satisfaction of the requirements to ontological data
• Generic editor
Amount Range ofproperties
Details Resamblence toreal data
Sat
isfa
ctio
n o
f re
qu
irem
ents
Satisfaction of the requirements to ontological data
• Generic editor
• Specialized editor
Amount Range ofproperties
Details Resamblence toreal data
Sat
isfa
ctio
n o
f re
qu
irem
ents
Satisfaction of the requirements to ontological data
• Generic editor
• Specialized editor
• Wrappers
Amount Range ofproperties
Details Resamblence toreal data
Sat
isfa
ctio
n o
f re
qu
irem
ents
Satisfaction of the requirements to ontological data
• Generic editor
• Specialized editor
• Wrappers
• GeneratorsAmount Range of
propertiesDetails Resamblence to
real data
Sat
isfa
ctio
n o
f re
qu
irem
ents
Evaluation of the method
• NAZOU (nazou.fiit.stuba.sk)
– Ontology consists of 740 classes (670 belong to taxonomies)
– All approaches used
• MAPEKUS (mapekus.fiit.stuba.sk)
– Ontology consists of 390 classes (360 belong to taxonomies)
– Only one approach used
Conclusions
• Solution for building ontologies for semantic Web application experimental evaluation
• Tunable method based on different approaches of ontology instance creation
• Evaluated in the domain of job offers and scientific publication
• Developed two SWEE ontologies
– Job offer ontology
– Publication metadata ontology