Proposed an Approach to Fit a Specialized Conceptual Model for Use in VGI System (Case Study: Surface Water Resources)

Today, volunteered geographic information (VGI) has provided an innovative, fast, and low-cost approach to data collection. Using this approach will lead to problems such as semantic heterogeneity. Ontology is one of the newest modeling scientific methods and that can solve the data semantic heterogeneity problems. But using ontology to collect information has challenges in volunteered geographic information systems. Since ontology is derived from the technical procedure so, for use it, specialized tools and specialized information are needed and its output will be used by experts. As people are not usually experts and do not have specialized tools, so in this research, we have proposed the method for utilizing ontology in VGI systems to prevent the creation of semantic heterogeneity in the data. Besides, volunteers don’t need to train or to have specialized tools for using this system. In this method, we are gathering information and developing the ontology model of information and finally an understandable conceptual model for the public at three levels of details using the classes, subclasses, relationships, and features in the ontology model will be built. Then we are relating this conceptual model to the ontological model using a look-up table. By doing this, the volunteers are entering their information in the VGI system based on the conceptual model, and the result of the ontology model will be provided to the decision makers as output. Finally, we selected the surface water resources as the case study and then evaluated the conceptual model from viewpoint of the simplicity and ambiguity of the volunteers and the lack of need for specialized information to use it. According to the results, approximately 83% of the concept model is unambiguous and understandable to volunteer without training. Also the results of the ontological model were evaluated in an efficiency viewpoint for decision making by experts. The results of this evaluation also are showing 88% of the output information from the ontology model is appropriate for decision making. Therefore, the results of the evaluating are showing the effectiveness of the conceptual and ontological model for the intended purpose.