The Semantic Web In Earth And Space Science Current Status And Future Directions
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The Semantic Web in Earth and Space Science. Current Status and Future Directions
The geosciences are one of the fields leading the way in advancing semantic technologies. This book continues the dialogue and feedback between the geoscience and semantic web communities. Increasing data volumes within the geosciences makes it no longer practical to copy data and perform local analysis. Hypotheses are now being tested through online tools that combine and mine pools of data. This evolution in the way research is conducted is commonly referred to as e-Science. As e-Science has flourished, the barriers to free and open access to data have been lowered and the need for semantics has been heighted. As the volume, complexity, and heterogeneity of data resources grow, geoscientists are creating new capabilities that rely on semantic approaches. Geoscience researchers are actively working toward a research environment of software tools and interfaces to data archives and services with the goals of full-scale semantic integration beginning to take shape. The members of this emerging semantic e-Science community are increasingly in need of semantic-based methodologies, tools and infrastructure. A feedback system between the geo- and computational sciences is forming. Advances in knowledge modeling, logic-based hypothesis checking, semantic data integration, and knowledge discovery are leading to advances in scientific domains, which in turn are validating semantic approaches and pointing to new research directions. We present mature semantic applications within the geosciences and stimulate discussion on emerging challenges and new research directions.
Integrating Relational Databases with the Semantic Web
An early vision in Computer Science was to create intelligent systems capable of reasoning on larg¬e amounts of data. Independent results in the areas of Semantic Web and Relational Databases have advanced us towards this vision. Despite independent advances, the interface between Relational Databases and Semantic Web is poorly understood. This dissertation revisits this early vision with respect to current technology and addresses the following question: How and to what extent can Relational Databases be integrated with the Semantic Web? The thesis is that much of the existing Relational Database infrastructure can be reused to support the Semantic Web. Two problems are studied. Can a Relational Database be automatically virtualized as a Semantic Web data source? The first contribution is an automatic direct mapping from a Relational Database schema and data to RDF and OWL. The second contribution is a method capable of evaluating SPARQL queries against the Relational Database by exploiting two existing relational query optimizations. These contributions are embodied in the Ultrawrap system. Experiments show that SPARQL query execution performance on Ultrawrap is comparable to that of SQL queries written directly for the relational data. Such results have not been previously achieved. Can a Relational Database be mapped to existing Semantic Web ontologies and act as a reasoner? A third contribution is a method for Relational Databases to support inheritance and transitivity by compiling the ontology as mappings, implementing the mappings as views, using SQL recursion and optimizing by materializing views. Ultrawrap is extended with this contribution. Empirical analysis reveals that Relational Databases are able to effectively act as reasoners.
Probabilistic Semantic Web
The management of uncertainty in the Semantic Web is of foremost importance given the nature and origin of the available data. This book presents a probabilistic semantics for knowledge bases, DISPONTE, which is inspired by the distribution semantics of Probabilistic Logic Programming. The book also describes approaches for inference and learning. In particular, it discusses 3 reasoners and 2 learning algorithms. BUNDLE and TRILL are able to find explanations for queries and compute their probability with regard to DISPONTE KBs while TRILLP compactly represents explanations using a Boolean formula and computes the probability of queries. The system EDGE learns the parameters of axioms of DISPONTE KBs. To reduce the computational cost, EDGEMR performs distributed parameter learning. LEAP learns both the structure and parameters of KBs, with LEAPMR using EDGEMR for reducing the computational cost. The algorithms provide effective techniques for dealing with uncertain KBs and have been widely tested on various datasets and compared with state of the art systems.