Abstract
Complex products, integrating many structures and functions, face challenges in fulfilling all requirements due to limited time and resources, making Requirements Prioritization (RP) essential in product development. The complexity of RP increases with the need to consider a wider range of criteria and data from more stakeholders like developers and customers, introducing uncertainty in requirements and expert opinions. However, current research rarely explores systematic methods for addressing requirements in this uncertain environment. Based on that, this paper presents a hybrid framework for organizing knowledge related to RP and determining item priorities. Specifically, we build a multi-dimensional evaluation indicator ontology, model requirement knowledge based on fuzzy RDF Knowledge Graph(KG), generate fuzzy membership degree through representation learning, and then rank requirements by fuzzy soft set. Finally, the effectiveness of our framework is validated in two aspects: evaluation of the fuzzy associative predicate representation learning method and application through a practical case study.
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