III-SGER: Spatio-Temporal-Thematic Queries of Semantic Web Data: a Study of Expressivity and Efficiency

Wright State University, Dayton OH

Investigators

Abstract

Spatial and temporal data are critical components in many applications. This is especially true in analytical applications ranging from scientific discovery to national security and criminal investigation. This exploratory research develops of new methods for modeling and querying spatial, temporal and thematic (STT) data. The methods differ significantly from traditional approaches for STT data management; they follow a paradigm that goes beyond querying for resources to querying about the relationships between resources. Three STT data management advances this will lead to are: (1) new query operators that exploit the graph-centric nature of Semantic Web data models, (2) new indexing and query processing techniques for STT data that are specialized for Semantic Web data models and (3) an extension of the SPARQL RDF query language to support STT queries. A second aspect of this project is to compare the STT-RDF approach described above with an alternative approach based on OWL-DL and qualitative spatial and temporal reasoning. This exploratory study evaluates whether the STT-RDF analytics approach provides a more efficient and expressive query language than the OWL-DL approach with space-time ontology. Specifically, (1) for the queries that can be encoded in both the formalisms, is the former implementation more efficient? and (2) are there queries that can be formulated in the STT-RDF analytics formalism that cannot be expressed as OWL-DL queries? Throughout this project, special attention is given to repeatability of experimental results. All code will be open source, and all benchmarks, datasets and ontologies will be available through the project web site at http://knoesis.wright.edu/research/semweb/projects/stt/