This chapter provides an overview of the thesis. It puts the selected papers (chapters II to XII) into perspective and links the topics addressed in each of the papers to each other. Wherever possible, references to specific sections of the selected papers are given.
Spatial data infrastructures will greatly benefit from the ability to compose geographic information (GI) services to solve complex problems. Discovering suitable services for data access and geoprocessing are major challenges in this endeavour. Current (keyword-based) approaches to service discovery are inherently restricted by the ambiguities of natural language, which can lead to low precision and/or recall. To alleviate these problems, we propose two ontology-based approaches for enhanced discovery of GI services. The approach for ontology-based discovery of data access services is based on semantic matchmaking between Description Logic (DL) concepts representing geographic feature types and the requester’s query. DL subsumption reasoning is used to find matches between queries and service descriptions. The approach for ontology-based discovery of geoprocessing services rests on two ideas. Ontologies describing geospatial operations are used to create descriptions of user requirements and service capabilities. Matches between these descriptions are identified based on function subtyping. In both approaches, service descriptions are based on a shared vocabulary that contains the basic terms of a domain and for which a shared understanding between the actors in the domain is assumed. We use a running example from the geospatial domain to analyse which problems can occur in existing keyword – and ontology-based approaches and how the discovery of GI services differs from other service discovery tasks. The example is also used for illustrating the prototypical implementation of the proposed approach.
The efficient use of distributed
geographic information is a key factor in planning and decision – making in a variety of domains. To facilitate the access to geographic information, spatial data infrastructures (SDIs) (Groot and McLaughlin, 2000; Masser, 2005) are currently being set up within regions, countries and across national borders (e. g. Bernard, 2002). Their main components are geographic information (GI) services providing access to geospatial data and geoprocessing capabilities. SDIs support users in discovering and accessing these services through catalogue services and syntactic interoperability standards.
In recent years, the number of GI services available on the Web has been rapidly and continually increasing. While at present, these services are generally isolated applications, their composability is often perceived as their greatest value as it enables more complex processing tasks (Einspanier et al., 2003). First attempts at composing GI services have been made. However, these rely on manually and statically coupling existing services (Bernard et al., 2003) while for future applications service composition is envisioned to be automated (Martin et al., 2004).
Discovering services that are appropriate for solving a specific problem from among a large number of available services is a central task within the larger task of service composition. Service discovery is essentially about finding a match between descriptions of service capabilities (i. e. of what the service provides) and user requirements (i. e. what is needed to solve a given problem) (Trastour et al., 2001).