Abstract:

In the face of increasingly rich knowledge resources, research on the matching of knowledge supply and demand is helpful to improve the effective allocation of knowledge resources, solve the problem of users’ “knowledge fascination”, and maximize the value of knowledge resources. It has important theoretical and practical value. This paper combines Pythagorean Fuzzy Sets（PFS） and fuzzy rough sets to establish the Pythagorean Fuzzy Rough Sets（PFRS） model to deal with the problem of knowledge supply and demand matching of fuzzy uncertain knowledge resources. By introducing the PFS correlation measure, it improves the attribute similarity, and finds the view similarity between user needs and case knowledge based on knowledge attribute weights to determine the matching result. Empirical analysis verifies the rationality and feasibility of the method proposed in this paper. Compared with existing methods, the introduction of Pythagorean fuzzy sets makes the description of fuzzy knowledge more objective and has certain theoretical advantages. At the same time, the weighted correlation coefficient is used to express the similarity of views, so that the matching result is more accurate and the matching accuracy is improved.

Key words: knowledge supply and demand matching, Pythagorean fuzzy sets, fuzzy rough sets, knowledge reuse

摘要：

面对日益丰富的知识资源，对知识供需匹配问题的研究有助于知识资源的有效配置、解决用户“知识迷向”问题，实现知识资源价值最大化，具有重要的理论意义和实践价值。将毕达哥拉斯模糊集（PFS）与模糊粗糙集相结合，建立毕达哥拉斯模糊粗糙集（PFRS）模型，用以处理模糊不确定性知识资源的知识供需匹配问题；通过引入PFS相关测度对属性相似度进行改进，并基于知识属性权重求出用户需求与案例知识之间的视图相似度，确定匹配结果。实证分析验证了所提方法的合理性与可行性，与既有方法相比，通过引入毕达哥拉斯模糊集，对模糊知识的描述更加客观，具备一定的理论优势；同时，用加权相关系数表示视图相似度，使匹配结果更加准确，提高了匹配精度。

关键词: 知识供需匹配, 毕达哥拉斯模糊集, 模糊粗糙集, 知识重用