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- 82 creator jie-bao.
- 82 creator li-ding.
- 82 type InProceedings.
- 82 label "Towards Social Webtops Using Semantic Wiki".
- 82 sameAs 82.
- 82 abstract "The fast-growing Web 2.0 applications promote explicit so- cial structure and explosive data on the Web, and enable the realization of social webtops, where users use web ap- plications to collaboratively organize and share online data. In this work, we show that semantic wiki technologies are suitable for building a social webtop. We also identify two key issues in realizing social webtop applications on seman- tic wiki and present our initial investigation with live demos: (i) popular concept modeling mechanisms used by webtop applications can be supported by semantic wiki; and (ii) provenance-aware data personalization mechanisms can be added as semantic wiki extensions to better support collab- orative data management on a webtop.".
- 82 hasAuthorList authorList.
- 82 isPartOf poster_demo_proceedings.
- 82 keyword "Concept Modeling".
- 82 keyword "Semantic Wiki".
- 82 keyword "Webtop".
- 82 title "Towards Social Webtops Using Semantic Wiki".
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- item-1 hasContent jie-bao.
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- item-3 hasContent deborah-mcguinness.
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- item-4 hasContent james-hendler.
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- 83 creator jens-lehmann.
- 83 creator sebastian-hellmann.
- 83 creator soeren-auer.
- 83 type InProceedings.
- 83 label "Learning of OWL Class Descriptions on Very Large Knowledge Bases".
- 83 sameAs 83.
- 83 abstract "The vision of the Semantic Web is to make use of semantic representations on the largest possible scale - the Web. Large knowledge bases such as DBpedia, OpenCyc, GovTrack, and others are emerging and are freely available as Linked Data and SPARQL endpoints. Exploring and analysing such knowledge bases is a significant hurdle for Semantic Web research and practice. As one possible direction for tackling this problem, we present an approach for obtaining complex class descriptions from objects in knowledge bases by using Machine Learning techniques. We describe how we leverage existing techniques to achieve scalability on large knowledge bases available as SPARQL endpoints or Linked Data. Our algorithms are made available in the open source DL-Learner project and can be used in real-life scenarios by Semantic Web applications.".
- 83 hasAuthorList authorList.
- 83 isPartOf poster_demo_proceedings.
- 83 keyword "Class Description".
- 83 keyword "Large Knowledge Bases".
- 83 keyword "Machine Learning".
- 83 keyword "OWL".
- 83 keyword "SPARQL".
- 83 title "Learning of OWL Class Descriptions on Very Large Knowledge Bases".
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- item-1 hasContent sebastian-hellmann.
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- 84 creator alberto-salvati.
- 84 creator aldo-gangemi.
- 84 creator enrico-daga.
- 84 creator valentina-presutti.
- 84 type InProceedings.
- 84 label "http://ontologydesignpatterns.org [ODP]".
- 84 sameAs 84.
- 84 abstract "We present ontologydesignpatterns.org (ODP), a semantic web portal about ontology design patterns based on wiki technology, which aims at supporting a community around best practices for ontology design. ODP offers services for evaluation and training about ontology patterns, and a repository of OWL ontologies. ODP fosters several kinds of participation, from anonymous, read-only access to open-rating and quality-committee membership. Based on semantic wiki components, we have developed EvalWF, an extension for supporting evaluation workflows able to manage the entire lifecycle of a pattern, from submission to certification.".
- 84 hasAuthorList authorList.
- 84 isPartOf poster_demo_proceedings.
- 84 keyword "Best Practices".
- 84 keyword "Ontology Design Pattern".
- 84 keyword "Ontology Evaluation".
- 84 keyword "Ontology Repository".
- 84 keyword "Semantic Wiki".
- 84 title "http://ontologydesignpatterns.org [ODP]".
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- item-1 hasContent valentina-presutti.
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- item-2 hasContent enrico-daga.
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