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- paper-28 label "SPARQLoid - a Querying System using Own Ontology and Ontology Mappings with Reliability".
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- paper-28 abstract "Heterogeneity of ontologies on the web of data is very important problem. To solve this problem, there are a lot of researches about ontology mapping/alignment/matching. This paper shows an application called SPARQLoid that is using a query rewriting method to enable the users to query any SPARQL endpoint with the users own ontology even when their mappings are not reliable enough. Often ontology matching is very difficult problem and it sometimes produces mappings under a certain reliability. Based on the given reliability degrees on those mappings, SPARQLoid allows users to query data in the target SPARQL endpoints by using their own (or a specified certain) ontology under a control of sorting order based on their mapping reliability.".
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- paper-28 title "SPARQLoid - a Querying System using Own Ontology and Ontology Mappings with Reliability".
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- paper-29 label "The Linked Data Visualization Model".
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- paper-29 abstract "The potential of the semantic data available in the Web is enormous but in most cases it is very difficult for users to explore and use this data. Applying information visualization techniques to the Semantic Web helps users to easily explore large amounts of data and interact with them. We devise a formal Linked Data Visualization model (LDVM), which allows to dynamically connect data with visualizations.".
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- paper-30 abstract "We propose a general framework to attach the licensing terms to the data where the compatibility of the licensing terms concerning the data affected by a query is verified, and, if compatible, the licenses are combined into a composite license. The framework returns the composite license as licensing term about the data resulting from the query.".
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- paper-30 title "Towards Licenses Compatibility and Composition in the Web of Data".
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- paper-31 label "TwikiMe! - User profiles that make sense".
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- paper-31 abstract "The use of social media has been rapidly increasing in the last years. Social media such as Twitter has become an important source of information for a variety of people. The public availability of data describing some of these social networks has led to a lot of research in this area. Link prediction, user classification and community detection are some of the main research areas related to social networks. In this paper we present a user modeling framework that uses Wikipedia as a frame to model user interests inside a social network. Our fine grained model of user interests reflects the areas a user is interested in as well as the level of expertise a user has in a certain field.".
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