Fuzzy Subsets Theory-Based Imprecision Modeling Using Ontology and Applied to Risk Estimation in Project Intelligent Management

The increasing complexity and dynamism of construction projects has imposed substantial uncertainties and subjectivities into the risk analysis process. Effective risk management involves a process that includes risk identification, risk assessment, risk response and risk monitoring. Risk assessment is already solved using fuzzy expert systems, entropic weighting or fuzzy linguistic multiple attribute decision making. Given the methods or algorithms absence for project risk management, we opt to solve this kind of problem through a fuzzy ontology where we were able to welcome the expertise of a few executives and projects managers in the project management domain. So, the objective of this paper is to develop a fuzzy ontology-based risk assessment model in intelligent project management compared to our expert system-based previous work which have used a translation scheme and contrary to the previous works in this domain and which focus on either fuzzy language-based works or fuzzy expert-based solution. To my knowledge, there has been no intelligent project management risk estimation study using both a fuzzy ontology and a syntax-driven translation scheme. For simplification reasons, we present in this paper only predicate fuzziness case of a simple fuzzy query. The first results are encouraging although the work is still in its early stages. To illustrate this solution, we use again the Cox work, based on the Metus System group managers' directives to determine the project risks.