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Consolidating User-defined Concepts with StYLiD

This paper discusses the proposed approach for consolidating user-defined concepts using the StYLiD system on the Social Semantic Web. It addresses the challenges of ontology creation, collaborative knowledge creation, and multiple conceptualizations. The StYLiD system allows ordinary people to share a wide variety of structured data and create informal lightweight ontologies.

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Consolidating User-defined Concepts with StYLiD

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  1. Consolidating User-defined Concepts with StYLiD Aman Shakya1, Hideaki Takeda1, Vilas Wuwongse2 1National Institute of Informatics Tokyo, Japan 2Asian Institute of Technology, Pathumthani, Thailand

  2. Outline • Introduction • Background • Social Semantic Web • Problems • Proposed approach • Overview • The StYLiD system • Concept Consolidation • Application Scenarios • Related Work • Conclusion and Future Work ASWC 2008, Bangkok, Thailand

  3. Background • People share data on the Web • Unstructured data • Structured Data • Model different types of “things” • Concepts, schemas – attributes and relations • Possible with Semantic Web technologies • Advantages • Semantic applications, automation, integration, interoperability, effective search and browsing ASWC 2008, Bangkok, Thailand

  4. Challenges • Long 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 ASWC 2008, Bangkok, Thailand

  5. Social Semantic Web • Social 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 ASWC 2008, Bangkok, Thailand

  6. Collaborative Knowledge Creation Collaborative Knowledge Base Users Users 4 Feb. 2009 ASWC 2008, Bangkok, Thailand

  7. Problems • Creation of data models satisfying many people and contexts simultaneously • Existence of Multiple Conceptualizations • Different user requirements, perspectives or contexts • But information exchange/integration should be possible • Consensus by collaborative interaction may be difficult and time-consuming • Still difficult for ordinary people • Considerable learning curve for existing systems • Restrictive constraints 4 Feb. 2009 ASWC 2008, Bangkok, Thailand

  8. Local KB Local KB Local KB Knowledge Sharing by Loose Collaboration Collaborative Knowledge Base Users Users Users 4 Feb. 2009 ASWC 2008, Bangkok, Thailand

  9. Objectives • To enable ordinary people to share a wide variety of structured data on the Semantic Web. • To allow multiple conceptualizations of the same concept by different people. • Consolidation of multiple user-defined concept schemas to form collaborative definitions. • To facilitate the emergence of informal lightweight ontologies. ASWC 2008, Bangkok, Thailand

  10. External Resources Overview Structured Data Collection Concept Consolidation Social Platform for Structured Data Authoring Schema Alignment Concepts Consolidated Concepts Concept groups Instances Concept Grouping Structured Linked Data Grouped concepts Browsing, Searching, Services Emerging Lightweight Ontologies User Community

  11. StYLiD • Structure 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 ASWC 2008, Bangkok, Thailand

  12. Creating a new Concept “Project” concept Attribute labels Description Suggested Value Range ASWC 2008, Bangkok, Thailand

  13. Enter Instance Data Literal value Suggested range concepts Resource URI Multiple Values ASWC 2008, Bangkok, Thailand

  14. 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 Concept Consolidation Virtual Concept Hotel Hotel (user3) Hotel (user1) Hotel (user2) • Allow multiple local conceptualizations • Aspects (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) ASWC 2008, Bangkok, Thailand

  15. Concept Consolidation • A concept consolidation C is defined as a triple < , S, A> where • - consolidated concept • S - set of constituent concepts {C1,C2 ,…..Cn} • Ais the attribute alignment between andS • 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 ASWC 2008, Bangkok, Thailand

  16. Concept Consolidation < , S, A> image view aligned( , ) aligned( , ) aligned( , ) A = { , … } 4 Feb. 2009 ASWC 2008, Bangkok, Thailand 16

  17. Concept Consolidation • Query 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 another • Translation of queries ASWC 2008, Bangkok, Thailand

  18. Concept Cloud Consolidated concept Sub-Cloud ASWC 2008, Bangkok, Thailand

  19. Alignment of Concept Schemas • Attribute 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 4 Feb. 2009 ASWC 2008, Bangkok, Thailand 19

  20. Concept versions x ASWC 2008, Bangkok, Thailand

  21. Structured Search Search on Consolidated Concept SPARQL ASWC 2008, Bangkok, Thailand

  22. Grouping Similar Concepts Suggest 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 concepts Visualize clusters of related concepts 4 Feb. 2009 ASWC 2008, Bangkok, Thailand 22

  23. Visualization of Concepts Grouping Cytoscape ASWC 2008, Bangkok, Thailand

  24. Grouping & Consolidation for Concept Generalization • Consolidation of related concepts as generalization • ( Hotel + Apartment ) => Accomodation • Multiple groupings possible with same concepts • ( Hotel + Restaurant ) => Eating place ASWC 2008, Bangkok, Thailand

  25. Linked Data • Link data instances • Select instance URIs as attribute value • Link to external data resources • Enter external URIs as attribute value • Link to Wikipedia contents StYLiD Wikipedia URI DBpedia URI (User friendly) (Machine friendly) ASWC 2008, Bangkok, Thailand

  26. Application Scenarios • Social Site for Structured Information Sharing StYLiD Users Concept Schemas CMS External Data Resources Structured data Data Integration Information Sharing Social Semantic Website Schema Alignment Users http://www.stylid.org

  27. Application Scenarios • Integrated Semantic portal IS1 Structured data Wrapper1 StYLiD IS2 Wrapper2 Data Backend Wrapper3 IS3 External Data Resources Concept Schemas Information Sources Data Integration Integrated Semantic Portal Schema Alignment Users Admin ASWC 2008, Bangkok, Thailand

  28. Related Work • Semantic Blogging • Semantic Wikis • Ontology from Folksonomy • Specia & Motta, 2007; Van Damme et al., 2007; Mika, 2007 • Schema alignment & Data integration Exhibit ASWC 2008, Bangkok, Thailand

  29. Conclusion • 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 ASWC 2008, Bangkok, Thailand

  30. Future Work • Computing hierarchical / non-hierarchical relations among concepts • Better schema alignment techniques • Consolidation of instances • Using / Mapping to existing vocabularies • Mash-ups / plugins to utilize structured data • Sharing scrapers to collect data from the web • … ASWC 2008, Bangkok, Thailand

  31. Thank You! • Questions • Comments ASWC 2008, Bangkok, Thailand

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