Semantically Enabled Temporal Reasoning in a Virtual Observatory

The Virtual Solar-Terrestrial Observatory (VSTO) is a distributed, scalable education and research environment for searching, integrating, and analyzing observational, experimental and model databases in fields of solar, solar-terrestrial and space physics. Our work on VSTO required us to create a formal, machine understandable representation for concepts, relations and attributes of physical quantities, including concepts to represent scientific instruments and their operating modes, parameters measured by these instruments, and measured from a particular location during a particular time period. The end-user is allowed to search for data based on measured parameters of interest, instruments of interest, geophysical constraints and temporal constraints. To fulfill this need we developed a set of formal encodings of the knowledge in the OWL-DL format. In the current implementation of VSTO (www.vsto.org), however, time coverage information is retrieved as needed from a relational database, not from the ontology. Consequently, the implementation is only able to perform temporal reasoning of the subsets of time intervals returned. This choice was made because it addressed our concerns, given the current level of the tools available at the time, of performance and scalability of creating millions of in-memory triples representing time coverage. The ideal solution would be to generate instances to represent any temporal relationships within the data. This would provide a greater range of temporal reasoning and search capabilities for the user. This paper will present information on how we are working to take the next step in this solution and to answer the questions of extending the expressiveness of the ontology to include more complex temporal reasoning; how this will impact the tools used to implement the ontology, how this will impact the implementation of the underlying data portal, and how this will impact the users of the system, the domain scientists, and their ability to effectively search for the data needed in a timely manner.