Abstract
In recent years, the rapid development of cloud computing and web technologies has led to a significant advancement to chain geospatial information services (GI services) in order to solve complex geospatial problems. However, the construction of a problem-solving workflow requires considerable expertise for end-users. Currently, few studies design a knowledge base to capture and share geospatial problem-solving knowledge. This paper abstracts a geospatial problem as a task that can be further decomposed into multiple subtasks. The task distinguishes three distinct granularities: Geooperator, Atomic Task, and Composite Task. A task model is presented to define the outline of problem solution at a conceptual level that closely reflects the processes for problem-solving. A task-oriented knowledge base that leverages an ontology-based approach is built to capture and share task knowledge. This knowledge base provides the potential for reusing task knowledge when faced with a similar problem. Conclusively, the details of implementation are described through using a meteorological early-warning analysis as an example.
Highlights
In recent years, with the rapid development of cloud computing and web technologies, an increasing number of geospatial information resources (GIRs), e.g., geospatial data, geospatial analysis functions, models, applications, etc., have been encapsulated into a wide variety of geographic information services (GIServices) [1] which are accessible to general public users over the web [2,3]
We focus on using ontologies in association with a task-oriented approach to construct a knowledge base to enhance geospatial problem-solving
The main work of this paper includes the following: (1) Concepts: the task concept is introduced as a reusable component for geospatial problem-solving and is used to reflect users’ requirements; (2) Model: a task model is proposed to simulate problem-solving processes; (3) Knowledge base: an ontological knowledge base is designed, that comprises several interoperable ontologies to capture and share problem-solving knowledge; and (4) Implementation: taking the meteorological early-warning (MEW) analysis, for example, we describe the details of the implementation conclusively
Summary
With the rapid development of cloud computing and web technologies, an increasing number of geospatial information resources (GIRs), e.g., geospatial data, geospatial analysis functions, models, applications, etc., have been encapsulated into a wide variety of geographic information services (GIServices) [1] which are accessible to general public users over the web [2,3]. The conceptual workflows can be formalized into a knowledge base, which can facilitate future users to solve the similar problems. We focus on using ontologies in association with a task-oriented approach to construct a knowledge base to enhance geospatial problem-solving. The main work of this paper includes the following: (1) Concepts: the task concept is introduced as a reusable component for geospatial problem-solving and is used to reflect users’ requirements; (2) Model: a task model is proposed to simulate problem-solving processes; (3) Knowledge base: an ontological knowledge base is designed, that comprises several interoperable ontologies to capture and share problem-solving knowledge; and (4) Implementation: taking the meteorological early-warning (MEW) analysis, for example, we describe the details of the implementation conclusively.
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