An ontology-based multicriteria spatial decision support system: a case study of house selection

Abstract

The paper proposes an ontology-based multicriteria spatial decision support system (MC-SDSS) for the house selection problem. The house selection ontology serves as a foundation for spatial multicriteria decision analysis (MCDA) in the house selection domain. It is built using the Web Ontology Language (OWL). The ontology represents the spatial MCDA knowledge associated with house selection using semantic machine-interpretable concepts and relationships in such a way that they can be used by machines not just for display purposes, but also for processing, automation, integration, and reuse across applications. It contains concepts (or classes) including quantitative and qualitative criteria (objectives and attributes), decision alternatives (houses for sale), criterion weights, and location attributes of the decision alternatives. The concepts are organized into a hierarchical classification structure using the Analytic Hierarchy Process. To evaluate the decision alternatives, a set of rules is implemented within the OWL knowledge base with the Semantic Web Rule Language. The rules are expressed as combinations of the OWL concepts and their properties. The paper illustrates an implementation of the proposed ontology-based MC-SDSS architecture using a case study of house selection in the City of Tehran, Iran.