Matches in ScholarlyData for { ?s ?p ?o. }
- 80 keyword "signing framework".
- 80 title "A Framework for Iterative Signing of Graph Data on the Web".
- authorList type List.
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- authorList hasItem item-1.
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- authorList hasItem item-3.
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- item-1 type ListItem.
- item-1 hasContent andreas-kasten.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent ansgar-scherp.
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- item-3 type ListItem.
- item-3 hasContent peter-schauss.
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- 81 creator luca-costabello.
- 81 type InProceedings.
- 81 label "Error-Tolerant RDF Subgraph Matching for Adaptive Presentation of Linked Data on Mobile".
- 81 sameAs 81.
- 81 abstract "We present PRISSMA, a context-aware presentation layer for Linked Data. PRISSMA extends the Fresnel vocabulary with the notion of mobile context. Besides, it includes an algorithm that determines whether the sensed context is compatible with some context declarations. The algorithm finds optimal error-tolerant subgraph isomorphisms between RDF graphs using the notion of graph edit distance and is sublinear in the number of context declarations in the system.".
- 81 hasAuthorList authorList.
- 81 isPartOf proceedings.
- 81 keyword "Context-Awareness".
- 81 keyword "Error-tolerant RDF matching".
- 81 keyword "Fresnel".
- 81 keyword "Linked Data".
- 81 title "Error-Tolerant RDF Subgraph Matching for Adaptive Presentation of Linked Data on Mobile".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
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- item-1 type ListItem.
- item-1 hasContent luca-costabello.
- 82 creator axel-cyrille-ngonga-ngomo.
- 82 creator klaus-lyko.
- 82 creator mohamed-sherif.
- 82 type InProceedings.
- 82 label "Unsupervised Link Discovery Through Knowledge Base Repair".
- 82 sameAs 82.
- 82 abstract "Over the last few years, devising efficient approaches to compute links between datasets has been regarded as central to achieve the vision behind the Data Web. Several unsupervised approaches have been developed to achieve this goal. Yet, so far, none of these approaches make use of replication of resources across several knowledge bases. In this paper, we present Colibri, an unsupervised approach that allows discovering links between datasets while improving the quality of the instance data in these datasets. A Colibri iteration begins by generating links between knowledge bases. Then, our approach makes use of these links to detect resources with probably erroneous or missing information. The erroneous or missing information detected by the approach is finally corrected or added. We evaluate our approach on benchmark datasets with respect to the F-score it achieves. Our results suggest that Colibri can improve the results of unsupervised machine-learning approaches for link discovery by up to 12% while correctly detecting and repairing erroneous resources.".
- 82 hasAuthorList authorList.
- 82 isPartOf proceedings.
- 82 keyword "Instance Repair".
- 82 keyword "Link Discovery".
- 82 keyword "Unsupervised Machine Learning".
- 82 title "Unsupervised Link Discovery Through Knowledge Base Repair".
- authorList type List.
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- authorList hasItem item-1.
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- authorList hasItem item-3.
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- item-1 type ListItem.
- item-1 hasContent axel-cyrille-ngonga-ngomo.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent mohamed-sherif.
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- item-2 previous item-1.
- item-3 type ListItem.
- item-3 hasContent klaus-lyko.
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- 84 creator bernardo-pereira-nunes.
- 84 creator besnik-fetahu.
- 84 creator davide-taibi.
- 84 creator marco-antonio-casanova.
- 84 creator stefan-dietze.
- 84 creator wolfgang-nejdl.
- 84 type InProceedings.
- 84 label "A Scalable Approach for Efficiently Generating Structured Dataset Topic Profiles".
- 84 sameAs 84.
- 84 abstract "The increasing adoption of Linked Data principles has led to an abundance of datasets on the Web. However, take-up and reuse is hindered by the lack of descriptive information about the nature of the data, such as their topic coverage, dynamics or evolution. To address this issue, we propose an approach for creating linked dataset profiles. A profile consists of structured dataset metadata describing topics and their relevance. Profiles are generated through the configuration of techniques for resource sampling from datasets, topic extraction from knowledge bases and their ranking based on graphical models. To enable a good trade-off between scalability and representatives of generated data, appropriate parameters are determined experimentally. Our evaluation considers topic profiles of all accessible datasets from the Linked Open Data cloud and shows that our approach generates representative profiles even with comparably small sample sizes (10%) outperforms established topic modelling approaches.".
- 84 hasAuthorList authorList.
- 84 isPartOf proceedings.
- 84 keyword "Linked Data".
- 84 keyword "Metadata".
- 84 keyword "Profiling".
- 84 keyword "Vocabulary of Links".
- 84 title "A Scalable Approach for Efficiently Generating Structured Dataset Topic Profiles".
- authorList type List.
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- authorList hasItem item-6.
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- item-1 type ListItem.
- item-1 hasContent besnik-fetahu.
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- item-2 type ListItem.
- item-2 hasContent stefan-dietze.
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- item-2 previous item-1.
- item-3 type ListItem.
- item-3 hasContent bernardo-pereira-nunes.