Although recent studies on the Semantic Web have focused on crisp ontologies and knowledge representation, they have paid less attention to imprecise knowledge. However, the results of these studies constitute a Semantic Web that can answer requests almost perfectly with respect to precision. Nevertheless, they ensure low recall. As such, we propose in this research work a new generic approach of fuzzification that which allows a semantic representation of crisp and fuzzy data in a domain ontology. In the framework of our real case study, the obtained illustrate that our approach is highly better than the crisp one in terms of completeness, comprehensiveness, generality, comprehension and shareability.

尽管最近对语义网的研究集中在清晰的本体和知识表示上，但他们对不精确的知识的关注较少。但是，这些研究的结果构成了一个语义网，可以在精度方面几乎完美地回答请求。但是，它们可确保召回率低。因此，我们在这项研究工作中提出了一种新的通用模糊化方法，该方法允许在域本体中以语义表示清晰和模糊的数据。在我们的实际案例研究的框架中，所获得的结果表明，我们的方法在完整性，全面性，通用性，理解性和共享性方面比脆性方法好得多。