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Consolidating User-defined Concepts with StYLiD. Aman Shakya 1 , Hideaki Takeda 1 , Vilas Wuwongse 2 1 National Institute of Informatics Tokyo, Japan 2 Asian Institute of Technology, Pathumthani , Thailand. Outline. Introduction Background Social Semantic Web Problems - PowerPoint PPT Presentation
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Consolidating User-Consolidating User-defined Concepts with defined Concepts with StYLiDStYLiD
Aman Shakya1, Hideaki Takeda1, Vilas Wuwongse2
1National Institute of Informatics Tokyo, Japan
2Asian Institute of Technology, Pathumthani, Thailand
OutlineOutlineIntroduction
◦ Background◦ Social Semantic Web◦ Problems
Proposed approach◦ Overview◦ The StYLiD system◦ Concept Consolidation◦ Application Scenarios
Related WorkConclusion and Future Work
4 Feb. 2009 ASWC 2008, Bangkok, Thailand 2
BackgroundBackgroundPeople share data on the Web
Unstructured data
Structured DataModel different types of “things”Concepts, schemas – attributes and
relationsPossible with Semantic Web technologiesAdvantages
Semantic applications, automation, integration, interoperability, effective search and browsing
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ChallengesChallengesLong Tail of information domains (Hunyh et al.
2007)◦ Wide variety of data to share
Not enough Ontologies
Ontology creation is a difficult process◦ Not feasible for every new type of data
Ontologies are difficult to understand and use◦ Semantic Web tech. too complex for ordinary people
4ASWC 2008, Bangkok, Thailand4 Feb. 2009
Social Semantic WebSocial Semantic WebSocial Software
◦ Easy to understand and use◦ Incremental & dynamic publishing platforms◦ Mass participation◦ Social interaction and collaboration
Semantic Web◦ Structured Data, automation / interoperability ,
etc.
Social software + Semantic Web◦ Social Semantic Web◦ Collaborative knowledge creation and sharing
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6
Collaborative Knowledge Creation
Collaborative Knowledge Base
Users Users
4 Feb. 2009 ASWC 2008, Bangkok, Thailand
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Problems1. Creation of data models satisfying many
people and contexts simultaneously
2. Existence of Multiple Conceptualizations◦ Different user requirements, perspectives or
contexts
◦ But information exchange/integration should be possible
3. Consensus by collaborative interaction may be difficult and time-consuming
4. Still difficult for ordinary people◦ Considerable learning curve for existing systems
◦ Restrictive constraints
4 Feb. 2009 ASWC 2008, Bangkok, Thailand
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Knowledge Sharing by Loose Collaboration
Collaborative Knowledge Base
Users
Users
Local KB
Local KB
Local KB
Users
4 Feb. 2009 ASWC 2008, Bangkok, Thailand
ObjectivesObjectives1. To enable ordinary people to share a wide
variety of structured data on the Semantic Web.
2. To allow multiple conceptualizations of the same concept by different people.
3. Consolidation of multiple user-defined concept schemas to form collaborative definitions.
4. To facilitate the emergence of informal lightweight ontologies.
4 Feb. 2009 ASWC 2008, Bangkok, Thailand 9
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Overview
Social Platformfor
Structured Data Authoring
Concept Grouping
External Resources
Concepts
Instances
Structured Data Collection
Browsing, Searching,Services
Concept groups
Concept Consolidation
Schema Alignment
Structured Linked Data Grouped
concepts
User Community
Consolidated Concepts
Emerging Lightweight Ontologies
StYLiDStYLiDStructure Your own Linked Data
http://www.stylid.org (get your account!)
Social Software for Sharing a wide variety of Structured Data
Users can freely define their own concepts Easy for ordinary people
◦ Flexible and relaxed interface for data entry
Consolidate Multiple Concept Schemas◦ To create rich concept definitions
Emerging informal ontologies◦ Popular concepts and evolving definitions
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Creating a new Concept
Attribute labels
Description
Suggested Value Range
“Project” concept
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Enter Instance Data
Literal value
Suggested range concepts
Resource URI
Multiple Values
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Concept ConsolidationConcept Consolidation
Hotel - ver.1 (user1)
Name
Address
Country
Hotel - ver.2 (user1)
Name
Address
Phone-number
Hotel - ver.3 (user1)
Name
Location
Rating
Hotel - ver.1 (user2)
Name
Capacity
Zip-code
Hotel - ver.2 (user2)
Name
Zip-code
Price
Hotel - ver.1 (user3)
Name
Lat
Long
Hotel (user1)
Hotel (user2)
Hotel (user3)
Hotel
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Virtual Concept
4 Feb. 2009
Allow multiple local conceptualizationsAspects (Takeda et al., 1995), DDL (Borgida and Serafini, 2003), Contextual ontologies, C-OWL (Bouquet et al., 2004), -connections (Kutz et al., 2004 ; Grau et al., 2004)
Concept ConsolidationConcept Consolidation
4 Feb. 2009 ASWC 2008, Bangkok, Thailand 15
A concept consolidation C is defined as a triple
< , S, A> where◦ - consolidated concept
◦ S - set of constituent concepts {C1,C2 ,…..Cn}
◦ A is the attribute alignment between and S
Based on Global-as-View (GAV) approach for data integration◦ Global schema defined as views on source schemas
Consolidated Concept with consolidated attributes◦ aligned to source concept attributes as views
CC
C
C
Concept ConsolidationConcept Consolidation
16
C1a2a
ma
iCaligned( , )
aligned( , )
1a 1ia2ia
inia
1ia
2a 2ia
aligned( , )ma inia
)( 1ia)( 2
ia
)( inia
view
1C
nC
iM
nM
1M
A = { , … }1M 2M nM
image
< , S, A>C
4 Feb. 2009 ASWC 2008, Bangkok, Thailand
Concept ConsolidationConcept ConsolidationQuery Unfolding (Advantage of GAV over LAV)
◦Queries over
to queries over {C1,C2 ,…..Cn}
◦Using alignment A◦Union of results
Translation of instances◦From one conceptualization to
anotherTranslation of queries
4 Feb. 2009 ASWC 2008, Bangkok, Thailand 17
C
Concept CloudConcept Cloud
Sub-Cloud
Consolidated concept
4 Feb. 2009 18ASWC 2008, Bangkok, Thailand
Alignment of Concept Alignment of Concept SchemasSchemasAttribute Alignments suggested Automatically
◦ Alignment API implementation with WordNet extension
Users verify and complete the alignment◦ Human intelligence + Machine intelligence
Alignments are represented and saved (for everyone)
◦ Alignment ontology (Hughes and Ashpole, 2004)
◦ Alignment API alignment specification language (Euzenat et al., 2007) Other formats : C-OWL, SWRL, OWL axioms, XSLT, SEKT-ML and
SKOS.
Incremental alignment
A Unified View◦ Consolidated concept with Consolidated Attributes◦ Homogenous table of data
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Concept versions
x
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Search on Consolidated Concept
SPARQL21ASWC 2008, Bangkok, Thailand
Structured SearchStructured Search
4 Feb. 2009
Grouping Similar ConceptsSuggest groups of similar concepts
◦ Under a similarity threshold
ConceptSim(C1, C2) = w1*NameSim(N1, N2) + w2*SchemaSim(S1, S2)
◦ NameSim - WordNet-based similarity (Lin’s algorithm)
◦ SchemaSim - Average similarity of best matching pairs of attributes
◦ Hungarian Algorithm - find best matching pairs (Kuhn,
1955; Munkres, 1957)
Browse groups of similar conceptsVisualize clusters of related concepts
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Visualization of Concepts Visualization of Concepts GroupingGrouping
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Cytoscape
Grouping & Consolidation for Grouping & Consolidation for Concept GeneralizationConcept Generalization
Consolidation of related concepts as generalization◦( Hotel + Apartment ) =>
Accomodation
Multiple groupings possible with same concepts◦( Hotel + Restaurant ) => Eating
place
04/21/2312/10/2008 ASWC 2008, Bangkok, Thailand 24
Linked DataLinked DataLink data instances
◦ Select instance URIs as attribute valueLink to external data resources
◦ Enter external URIs as attribute value
Link to Wikipedia contents
ASWC 2008, Bangkok, Thailand 25
StYLiStYLiDD
Wikipedia URI DBpedia URI
4 Feb. 2009
(User friendly) (Machine friendly)
Application ScenariosSocial Site for
Structured Information Sharing
Concept Schemas
Structured data
External Data
Resources
StYLiD
CMS
Data IntegrationSchema
Alignment
Information Sharing Social
Semantic Website
Users
Usershttp://www.stylid.org
Application ScenariosIntegrated Semantic portal
4 Feb. 2009 ASWC 2008, Bangkok, Thailand 27
Structured data
External Data
Resources
StYLiD
Data Backend
Data IntegrationSchema
Alignment
Integrated Semantic
Portal
UsersAdmin
Concept Schemas
IS1
IS2
IS3
Wrapper1
Wrapper2
Wrapper3
Information Sources
Related WorkRelated Work
Semantic Blogging
Semantic Wikis
Ontology from Folksonomy◦ Specia & Motta, 2007; Van Damme et al., 2007; Mika, 2007
Schema alignment & Data integration
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Exhibit
ConclusionConclusion
ASWC 2008, Bangkok, Thailand 29
Consolidation of multiple concepts◦ Allow multiple conceptualizations◦ Loose collaborative approach for concept
definition◦ Data Integration for the Semantic Web
StYLiD◦ Social software for sharing wide variety of
structured Semantic Web data◦ Easy for ordinary users to contribute freely
Emergent lightweight informal ontologies◦ Evolution, Consolidation and Grouping◦ Ontology as by-product of information sharing
4 Feb. 2009
Future WorkFuture Work
ASWC 2008, Bangkok, Thailand 30
Computing hierarchical / non-hierarchical relations among concepts
Better schema alignment techniquesConsolidation of instancesUsing / Mapping to existing
vocabulariesMash-ups / plugins to utilize structured
dataSharing scrapers to collect data from
the web…
4 Feb. 2009
Thank You!Thank You!
ASWC 2008, Bangkok, Thailand 31
QuestionsComments
4 Feb. 2009
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