Matches in ScholarlyData for { ?s ?p ?o. }
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- 33 creator carlos-pedrinaci.
- 33 creator jorge-cardoso.
- 33 creator torsten-leidig.
- 33 type InProceedings.
- 33 label "Linked USDL: a Vocabulary for Web-scale Service Trading".
- 33 sameAs 33.
- 33 abstract "An increasing number of services and marketplaces are flourishing in the Internet. From human services advertised in Google Helpouts to cloud services available at Amazon AWS marketplace. All services are, however, solely described in unstructured Web pages, which are suitable for manual browsing, but are difficult to exploit for creating advanced software applications which can foster service trading by supporting automated search, selection, service level negotiation, and contracting. The Unified Service Description Language (USDL) aimed at formalising service descriptions to take the current Internet to a Web of services. Nonetheless, its rigid technological foundations, its high complexity, and the reduced extensibility of the model limited its adoption. Informed from past experience on USDL, we present in this paper Linked USDL, the next evolution of USDL that adopts and exploits Linked Data to be scalable for the Web, to promote and simplify its adoption by reusing vocabularies and datasets, and to benefit from a high level of genericity and adaptability for domain specific modelling.".
- 33 hasAuthorList authorList.
- 33 isPartOf proceedings.
- 33 keyword "Linked Data".
- 33 keyword "Services".
- 33 keyword "USDL".
- 33 keyword "Vocabulary".
- 33 keyword "eCommerce".
- 33 title "Linked USDL: a Vocabulary for Web-scale Service Trading".
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- item-1 hasContent carlos-pedrinaci.
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- 35 creator anastasia-analyti.
- 35 creator christina-lantzaki.
- 35 creator thanos-yannakis.
- 35 creator yannis-tzitzikas.
- 35 type InProceedings.
- 35 label "Generating Synthetic RDF Data with Connected Blank Nodes for Benchmarking".
- 35 sameAs 35.
- 35 abstract "Generators for synthetic RDF datasets are very important for testing and benchmarking various semantic data management tasks (e.g. querying, storage, update, compare, integrate). However, the current generators, like the Univ-Bench Artificial data generator (UBA), do not support sufficiently (or totally ignore) blank node connectivity issues. Blank nodes are used for various purposes (e.g. for describing complex attributes), and a significant percentage of resources is currently represented with blank nodes. Moreover, several semantic data management tasks, like isomorphism checking (useful for checking equivalence), and blank node matching (useful in comparison/versioning/synchronization, and in semantic similarity functions), not only have to deal with blank nodes, but their complexity and optimality depends on the connectivity of blank nodes. To enable the comparative evaluation of the various techniques for carrying out these tasks, in this paper we present the design and implementation of a generator, called BGEN, which allows building data sets containing blank nodes with the desired complexity, controllable through various features (morphology, size, diameter, clustering coefficient). Finally, the paper reports experimental results concerning the efficiency of the generator, as well as results from using the generated datasets, that demonstrate the value of the generator.".
- 35 hasAuthorList authorList.
- 35 isPartOf proceedings.
- 35 keyword "Blank nodes and equivalence testing and comparison (diff)".
- 35 keyword "Connectivity of Blank nodes".
- 35 keyword "Generation of synthetic RDF data for benchmarking".
- 35 title "Generating Synthetic RDF Data with Connected Blank Nodes for Benchmarking".
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- 37 creator bei-xu.
- 37 creator huakang-li.
- 37 creator mingyi-guo.
- 37 creator yi-liu.
- 37 type InProceedings.
- 37 label "A Knowledge Based Approach for Tackling Mislabeled Multi-class Big Social Data".
- 37 sameAs 37.
- 37 abstract "The performance of classification models extremely relies on the quality of training data. However, label imperfection is an inherent fault of training data, which cannot be manually handled in big data environment. Various methods have been proposed to remove label noises in order to improve classification quality, with the side effect of cutting down data bulk. In this paper, we propose a knowledge based approach for tackling mislabeled multi-class big data, in which knowledge graph technique is combined with other data correction method to perceive and correct the error labels in big data. Experiments on a medical Q&A social data set show our knowledge graph based approach can effectively improve data quality and classification accuracy. Furthermore, this approach can be applied in other data mining tasks requiring deep understanding.".
- 37 hasAuthorList authorList.
- 37 isPartOf proceedings.
- 37 keyword "classification".
- 37 keyword "knowledge graph".
- 37 keyword "label correction".
- 37 keyword "label imperfection".
- 37 title "A Knowledge Based Approach for Tackling Mislabeled Multi-class Big Social Data".
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- item-1 hasContent huakang-li.
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