004 Datenverarbeitung; Informatik
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Iterative Signing of RDF(S) Graphs, Named Graphs, and OWL Graphs: Formalization and Application
(2013)
When publishing graph data on the web such as vocabulariesrnusing RDF(S) or OWL, one has only limited means to verify the authenticity and integrity of the graph data. Today's approaches require a high signature overhead and do not allow for an iterative signing of graph data. This paper presents a formally defined framework for signing arbitrary graph data provided in RDF(S), Named Graphs, or OWL. Our framework supports signing graph data at different levels of granularity: minimum self-contained graphs (MSG), sets of MSGs, and entire graphs. It supports for an iterative signing of graph data, e. g., when different parties provide different parts of a common graph, and allows for signing multiple graphs. Both can be done with a constant, low overhead for the signature graph, even when iteratively signing graph data.
Model-Driven Engineering (MDE) aims to raise the level of abstraction in software system specifications and increase automation in software development. Modelware technological spaces contain the languages and tools for MDE that software developers take into consideration to model systems and domains. Ontoware technological spaces contain ontology languages and technologies to design, query, and reason on knowledge. With the advent of the Semantic Web, ontologies are now being used within the field of software development, as well. In this thesis, bridging technologies are developed to combine two technological spaces in general. Transformation bridges translate models between spaces, mapping bridges relate different models between two spaces, and, integration bridges merge spaces to new all-embracing technological spaces. API bridges establish interoperability between the tools used in the space. In particular, this thesis focuses on the combination of modelware and ontoware technological spaces. Subsequent to a sound comparison of languages and tools in both spaces, the integration bridge is used to build a common technological space, which allows for the hybrid use of languages and the interoperable use of tools. The new space allows for language and domain engineering. Ontology-based software languages may be designed in the new space where syntax and formal semantics are defined with the support of ontology languages, and the correctness of language models is ensured by the use of ontology reasoning technologies. These languages represent a core means for exploiting expressive ontology reasoning in the software modeling domain, while remaining flexible enough to accommodate varying needs of software modelers. Application domains are conceptually described by languages that allow for defining domain instances and types within one domain model. Integrated ontology languages may provide formal semantics for domain-specific languages and ontology technologies allow for reasoning over types and instances in domain models. A scenario in which configurations for network device families are modeled illustrates the approaches discussed in this thesis. Furthermore, the implementation of all bridging technologies for the combination of technological spaces and all tools for ontology-based language engineering and use is illustrated.
Querying for meta knowledge
(2008)
The Semantic Web is based on accessing and reusing RDF data from many different sources, which one may assign different levels of authority and credibility. Existing Semantic Web query languages, like SPARQL, have targeted the retrieval, combination and reuse of facts, but have so far ignored all aspects of meta knowledge, such as origins, authorship, recency or certainty of data, to name but a few. In this paper, we present an original, generic, formalized and implemented approach for managing many dimensions of meta knowledge, like source, authorship, certainty and others. The approach re-uses existing RDF modeling possibilities in order to represent meta knowledge. Then, it extends SPARQL query processing in such a way that given a SPARQL query for data, one may request meta knowledge without modifying the query proper. Thus, our approach achieves highly flexible and automatically coordinated querying for data and meta knowledge, while completely separating the two areas of concern.