首页|知识图谱强化网络分析法的系统评价方法

知识图谱强化网络分析法的系统评价方法

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针对传统系统评价方法在评价指标体系构建和权重分配过程中过于依赖专家主观意见的缺点,利用网络分析法(ANP)结构与知识图谱结构的相似性,提出一种基于知识图谱的网络分析强化方法,充分使用先验知识,增强评价的完备性和客观性。首先,收集评价任务相关的文本数据建立知识图谱,作为网络分析法的网络层指标库;然后,依据评价任务确定网络分析法的控制层,包括评价目标和评价准则。根据控制层,在网络层指标库中搜索适配的指标构建网络层;接着,以指标与准则间的相似度为客观度标准,调整指标相对重要性,并通过网络分析法计算各指标的权重,依据各指标的得分完成系统评价;最后,应用案例的结果验证该方法具有有效性、先进性以及通用性。
System evaluation method of analytic network process strengthened by knowledge graph
Aiming at the shortcomings of traditional system evaluation methods that rely too much on the subjective opinions in the construction of an evaluation indicator system and weight distribution,this article utilizes the similarity between the structure of analytic network process(ANP)and the structure of knowledge graph to proposes a strengthened analytic network process method based on knowledge graph.This method adopts prior knowledge to enhance the completeness and objectivity of the evaluation.Firstly,the text data related to the evaluation task is collected to establish the knowledge graph which is the indicator library of the network layer.Secondly,the control layer of the analytic network process that includes the evaluation objective and the evaluation criteria is determined according to the evaluation task.The network layer is constructed by searching for suitable indicators in the network layer indicator library according to the control layer.Thirdly,the similarity between the indicators and the criteria is adopted as the objective standard to adjust the relative importance of the indicators.Through the analytic network process,the weight of each indicator is calculated to complete the system evaluation based on the scrore of each indicator.Finally,the results of the application cases verify that the proposed method has effectiveness,advanced nature and universality.

system evaluationknowledge graphanalytic network processevaluation indicatorindicator weightsimilarity

刘剑慰、邢健豪、姜斌、冒泽慧、马亚杰

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南京航空航天大学自动化学院,南京 211100

系统评价 知识图谱 网络分析法 评价指标 指标权重 相似度

国家重点研发计划项目

2020AAA0109305

2024

控制与决策
东北大学

控制与决策

CSTPCD北大核心
影响因子:1.227
ISSN:1001-0920
年,卷(期):2024.39(1)
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