Abstract
Intelligent geoprocessing relies heavily on formalized parameter constraints of geoprocessing tools to validate the input data and to further ensure the robustness and reliability of geoprocessing. However, existing methods developed to formalize parameter constraints are either designed based on ill-suited assumptions, which may not correctly identify the invalid parameter inputs situation, or are inefficient to use. This paper proposes a novel method to formalize the parameter constraints of geoprocessing tools, based on a high-level and standard constraint language (i.e., SHACL) and geoprocessing ontologies, under the guidance of a systematic classification of parameter constraints. An application case and a heuristic evaluation were conducted to demonstrate and evaluate the effectiveness and usability of the proposed method. The results show that the proposed method is not only comparatively easier and more efficient than existing methods but also covers more types of parameter constraints, for example, the application-context-matching constraints that have been ignored by existing methods.
Highlights
In the last two decades, intelligent geoprocessing based on Semantic Web technologies such as the Resource Description Framework (RDF) and ontology has shown promise to significantly improve the efficiency and effectiveness of the discovery, composition, and execution of geoprocessing tools [1,2,3,4,5,6]
This study proposes a method of formalizing parameter constraints based on a standard high-level RDF constraint language, i.e., the Shapes Constraint Language (SHACL) [27]
Input data validation based on formalized parameter constraints is a key step in intelligent geoprocessing to ensure the correct functioning of geoprocessing tools and the reliability of results
Summary
In the last two decades, intelligent geoprocessing based on Semantic Web technologies such as the Resource Description Framework (RDF) and ontology has shown promise to significantly improve the efficiency and effectiveness of the discovery, composition, and execution of geoprocessing tools (especially geoprocessing web services) [1,2,3,4,5,6]. Intelligent geoprocessing relies heavily on end-users’ understanding of the tools and trial-and-errors to validate the parameter inputs [2,7,8,12] (Figure 1c) This constraints are important in the discovery and composition of non-experts. Intelligent geoprocessing relies heavily on end-users’ understanding damental research issue for automated validation of parameter inputs is how to explicitly of the tools and trial-and-errors to validate the parameter inputs [2,7,8,12] (Figure 1c) This formalize the and parameter constraintsfor into a machine-readable form (typically, RDF graphs), is frustrating time-consuming end-users, especially for non-experts. Parameter constraints as ontology restrictions (e.g., owl:minCardinality) by using ontology languages (such as the Web Ontology Language, OWL) [13–
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