基于知识元引用网络的细分领域演化特征研究

Research on the Evolution Characteristics of Subdivision Fields Based on Knowledge Unit Citation Networks

[目的/意义]理解科学知识发展演化过程有助于促进科学研究,从微观视角追踪知识在细分领域中的结构与演化特征对知识评估和知识服务具有重要意义。[方法/过程]以医学信息学中知识元为例,利用语义类型界定每种疾病的治疗相关细分领域,构建125种疾病不同时刻的知识元引用网络,采用Leiden算法识别知识群落,从群落知识演化、群落知识竞争状态等维度揭示疾病个体的演化特征;提出丰富度、均衡性与差异度三种测度指标,揭示疾病个体与整体的多样性特征。[结果/结论]研究表明,知识元引用网络中的群落能够反映出疾病知识结构与演进状态。整体层次的疾病知识多样性特征包括:疾病知识群落数量不断增加,群落间规模与组成差异不断扩大;不同疾病表现出常规型、早期争议型、泛指型演化模式;研究较早的疾病普遍均衡性较低、差异度较高。

[Purpose/significance]Comprehending the development process of scientific knowledge contributes to the scientific research.For knowledge evaluation and service,it is crucial to trace the structure and progress of knowledge in subdivided fields from a micro perspective.[Method/process]This article took the knowledge unit in medical informatics as an example.This paper used the semantic type of triples to define the treatment-related subdivision fields,constructed the knowledge unit citation networks of 125 diseases at different intervals,and identified the knowledge communities with the Leiden algorithm.From the dimensions of knowledge evolution and knowledge competition state,we aim to reveal the evolutionary characteristics of disease individuals.The indexes of Richness,Balance,and Difference are calculated to reveal the diversity characteristics of disease individuals as well as the overall disease population.[Result/conclusion]The research demonstrates that the knowledge communities can reflect the knowledge structure and evolution state of disease individuals.The overall diversity characteristics of diseases include:the commonality of indicators indicates that the number of all disease knowledge communities is increasing,and the differences in scale and composition between communities are expanding.Different diseases show conventional,early-controversial,and generalized evolutionary patterns,with the earlier diseases being less balanced and more different.