rdf

Abstract: Both the Resource Description Framework (RDF), used in the semantic web, and Maya Viz u-forms represent data as a graph of objects connected by labeled edges. Existing systems for flexible visualization of this kind of data require manual specification of the possible visualization roles for each data attribute. When the schema is large and unfamiliar, this requirement inhibits exploratory visualization by requiring a costly up-front data integration step. To eliminate this step, we propose an automatic technique for mapping data attributes to visualization attributes. We formulate this as a schema matching problem, finding appropriate paths in the data model for each required visualization attribute in a visualization template.

 Visualization;  data integration;  semantic;  infovis;  data-mining;  rdf;  attribute inference;  

Abstract: Both the Resource Description Framework (RDF), used in the semantic web, and Maya Viz u-forms represent data as a graph of objects connected by labeled edges. Existing systems for flexible visualization of this kind of data require manual specification of the possible visualization roles for each data attribute. When the schema is large and unfamiliar, this requirement inhibits exploratory visualization by requiring a costly up-front data integration step. To eliminate this step, we propose an automatic technique for mapping data attributes to visualization attributes. We formulate this as a schema matching problem, finding appropriate paths in the data model for each required visualization attribute in a visualization template.

 Visualization;  data integration;  semantic;  infovis;  data-mining;  rdf;  attribute inference;  

Abstract: Semantic Web browsers and other tools aimed at displaying RDF data to end users are all concerned with the same problem: presenting content primarily intended for machine consumption in a human-readable way. Their solutions differ but in the end address the same two high-level issues, no matter the underlying representation paradigm: specifying (i) what information contained in RDF models should be presented (content selection) and (ii) how this information should be presented (content formatting and styling). However, each tool currently relies on its own ad hoc mechanisms and vocabulary for specifying RDF presentation knowledge, making it difficult to share and reuse such knowledge across applications. Recognizing the general need for presenting RDF content to users and wanting to promote the exchange of presentation knowledge, we designed Fresnel as a browser-independent vocabulary of core RDF display concepts. In this paper we describe Fresnel’s main concepts and present several RDF browsers and visualization tools that have adopted the vocabulary so far.

 ontology;  rdf;  semanticweb;  

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Abstract: Facet browsing has become popular as a user friendly interface to data repositories. The Semantic Web raises new challenges due to the heterogeneous character of the data. First, users should be able to select and navigate through facets of resources of any type and to make selections based on properties of other, semantically related, types. Second, where traditional facet browsers require manual configuration of the software, a semantic web browser should be able to handle any RDFS dataset without any additional configuration. Third, hierarchical data on the semantic web is not designed for browsing: complementary techniques, such as search, should be available to overcome this problem. We address these requirements in our browser, /facet. Additionally, the interface allows the inclusion of facet-specific display options that go beyond the hierarchical navigation that characterizes current facet browsing. /facet is a tool for Semantic Web developers as an instant interface to their complete dataset. The automatic facet configuration generated by the system can then be further refined to configure it as a tool for end users. The implementation is based on current Web standards and open source software. The new functionality is motivated using a scenario from the cultural heritage domain.

 rdf;  semanticweb;  

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Abstract: RDF and RDF Schema are two W3C standards aimed at enriching the Web with machine-processable semantic data. We have developed Sesame, an architecture for efficient storage and expressive querying of large quantities of metadata in RDF and RDF Schema. Sesames design and implementation are independent from any specific storage device. Thus, Sesame can be deployed on top of a variety of storage devices, such as relational databases, triple stores, or object-oriented databases, without having to change the query engine or other functional modules. Sesame offers support for concurrency control, independent export of RDF and RDFS information and a query engine for RQL, a query language for RDF that offers native support for RDF Schema semantics. We present an overview of Sesame as a generic architecture, as well as its implementation and our first experiences with this implementation.

 query;  semantic;  rdf;  

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Abstract: Building repositories for e-learning is an iterative process and course content and course structure are always changing. We realized the necessity to separate content from structure of a given course during the conception of our first e-learning repository, which we called KBS-Hyperbook, several years ago at our institute. This system has been built around a conceptual model for structure and contents of the domain, which is expressed in the O-Telos conceptual modelling language. To ease exchange of metadata between such repositories, the Open Learning Repository (OLR), an e-learning repository we built during the last year to experiment with various features useful for such repositories, has been developed using RDF/RDFS as modelling language. In the first part of this paper, we describe the OLR system in more detail, and show how it uses RDF/RDFS as its underlying modelling language to express information about the learning objects contained in the repository, as well as information about the relationships between these learning objects. Based on our experience in meta-modelling using different modelling languages, we will in the second part of this paper discuss RDF/RDFS and O-Telos modelling in more depth and will analyse similarities and differences of these two modelling languages.

 eLearning;  rdf;  

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