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Matches in ScholarlyData for { ?s ?p ?o. }

• item-1 type ListItem.
• item-1 hasContent guang-xiang.
• item-1 next item-2.
• item-2 type ListItem.
• item-2 hasContent jason-i-hong.
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• 59 type InProceedings.
• 59 label "Rapid Prototyping of Semantic Mash-Ups through Semantic Web Pipes".
• 59 sameAs 59.
• 59 abstract "The use of RDF data published on the Web for applica- tions is still a cumbersome and resource-intensive task due to the limited software support and the lack of standard pro- gramming paradigms to deal with everyday problems such as combination of RDF data from different sources, object iden- tifier consolidation, ontology alignment and mediation, or plain querying and filtering tasks. In this paper we present Semantic Web Pipes that support fast implementation of Se- mantic data mash-ups while preserving desirable properties such as abstraction, encapsulation, component-orientation, code re-usability and maintainability which are common and well supported in other application areas.".
• 59 hasAuthorList authorList.
• 59 isPartOf proceedings.
• 59 keyword "Semantic/Data Web".
• 59 title "Rapid Prototyping of Semantic Mash-Ups through Semantic Web Pipes".
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• item-1 type ListItem.
• item-1 hasContent danh-le-phuoc.
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• item-2 type ListItem.
• item-2 hasContent axel-polleres.
• item-2 next item-3.
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• item-3 type ListItem.
• item-3 hasContent manfred-hauswirth.
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• item-4 type ListItem.
• item-4 hasContent giovanni-tummarello.
• item-4 next item-5.
• item-4 previous item-3.
• item-5 type ListItem.
• item-5 hasContent christian-morbidoni.
• item-5 previous item-4.
• 6 type InProceedings.
• 6 label "Learning to Recognize Reliable Users and Content in Social Media with Coupled Mutual Reinforcement".
• 6 sameAs 6.
• 6 abstract "Community Question Answering (CQA) has emerged as a popular forum for users to pose questions for other users to answer. Over the last few years, CQA portals such as Naver and Yahoo! Answers have exploded in popularity, and now provide a viable alternative to general purpose Web search. At the same time, the answers to past questions submitted in CQA sites comprise a valuable knowledge repository which could be a gold mine for information retrieval and automatic question answering. Unfortunately, the quality of the submitted questions and answers varies widely - increasingly so that a large fraction of the content is not usable for answering queries. Previous approaches for retrieving relevant and high quality content have been proposed, but they require large amounts of manually labeled data – which limits the applicability of the supervised approaches to new sites and domains. In this paper we address this problem by developing a semi-supervised coupled mutual reinforcement framework for simultaneously calculating content quality and user reputation, that requires relatively few labeled examples to initialize the training process. Results of a large scale evaluation demonstrate that our methods are more effective than previous approaches for finding high-quality answers, questions, and users. More importantly, our quality estimation significantly improves the accuracy of search over CQA archives over the state-of-the-art methods.".
• 6 hasAuthorList authorList.
• 6 isPartOf proceedings.
• 6 keyword "Data Mining".
• 6 title "Learning to Recognize Reliable Users and Content in Social Media with Coupled Mutual Reinforcement".
• authorList type List.
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• item-1 type ListItem.
• item-1 hasContent jiang-bian.
• item-1 next item-2.
• item-2 type ListItem.
• item-2 hasContent yandong-liu.
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• item-2 previous item-1.
• item-3 type ListItem.
• item-3 hasContent ding-zhou.
• item-3 next item-4.
• item-3 previous item-2.
• item-4 type ListItem.
• item-4 hasContent eugene-agichtein.
• item-4 next item-5.
• item-4 previous item-3.
• item-5 type ListItem.
• item-5 hasContent hongyuan-zha.
• item-5 previous item-4.
• 60 type InProceedings.
• 60 label "idMesh: Graph-Based Disambiguation of Linked Data".
• 60 sameAs 60.
• 60 abstract "We tackle the problem of disambiguating entities on the Web. We propose a user-driven scheme where graphs of entities – represented by globally identifiable declarative artifacts – self-organize in a dynamic and probabilistic manner. Our solution has the following two desirable properties: i) it lets end-users freely define associations between arbitrary entities and ii) it probabilistically infers entity relationships based on uncertain links using constraintsatisfaction mechanisms. We outline the interface between our scheme and the current data Web, and show how higher-layer applications can take advantage of our approach to enhance search and update of information relating to online entities. We describe a decentralized infrastructure supporting efficient and scalable entity disambiguation and demonstrate the practicability of our approach in a deployment over several hundreds of machines.".
• 60 hasAuthorList authorList.
• 60 isPartOf proceedings.
• 60 keyword "Semantic/Data Web".
• 60 title "idMesh: Graph-Based Disambiguation of Linked Data".
• authorList type List.
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• item-1 type ListItem.
• item-1 hasContent philippe-cudre-mauroux.
• item-1 next item-2.
• item-2 type ListItem.
• item-2 hasContent parisa-haghani.
• item-2 next item-3.
• item-2 previous item-1.
• item-3 type ListItem.
• item-3 hasContent michael-jost.
• item-3 next item-4.

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