Rules and ontologies are widely used in software development since they provide semantic web applications with meaning and reasoning features. This demonstration paper presents InSCo-Gen, a Model-Driven Development (MDD) tool for Web rule-based applications, which constructs a functional Web architecture integrating a rule engine for reasoning tasks. Development process is based on conceptual models composed of ontologies and production rules. These models are the source for the MDD process, which automatically generates implementation of the Web application.
Rule languages and inference engines incorporate reasoning capabilities to Web information systems. This paper presents an approach for the specification and development of Web applications performing the usual functionalities of data management and incorporating a rule engine for reasoning capabilities. The proposed approach is based on the definition of a high-level representation of the semantics of rule-based applications through a formalism for conceptual modeling combining lightweight ontologies and production rules. These models are used as the source for a model-driven method that applies several transformations to conceptual models generating the rule-based Web application code in an automatic process. As a result, the rule-based Web application embeds a rule engine suitable for deducing information by applying an inference process. The structure of the information managed by the Web application is based on ontology classes, whereas the logical expressions applied in reasoning are obtained from production rules of the model. A rule-based Web application has been developed and evaluated using a supporting tool that implements the ideas presented in this paper.
Current practices in agricultural management involve the application of rules and techniques to ensure high quality and environmentally friendly production. Based on their experience, agricultural technicians and farmers make critical decisions affecting crop growth while considering several interwoven agricultural, technological, environmental, legal and economic factors. In this context, decision support systems and the knowledge models that support them, enable the incorporation of valuable experience into software systems providing support to agricultural technicians to make rapid and effective decisions for efficient crop growth. Pest control is an important issue in agricultural management due to crop yield reductions caused by pests and it involves expert knowledge. This paper presents a formalisation of the pest control problem and the workflow followed by agricultural technicians and farmers in integrated pest management, the crop production strategy that combines different practices for growing healthy crops whilst minimising pesticide use. A generic decision schema for estimating infestation risk of a given pest on a given crop is defined and it acts as a metamodel for the maintenance and extension of the knowledge embedded in a pest management decision support system which is also presented. This software tool has been implemented by integrating a rule-based tool into web-based architecture. Evaluation from validity and usability perspectives concluded that both agricultural technicians and farmers considered it a useful tool in pest control, particularly for training new technicians and inexperienced farmers.
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