本研究以增長層級式自我組織映射圖(Growing Hierarchical Self-Organizing Map, GHSOM)分析出利他研究文獻中重要的歷年研究主題及概念交涉關係。本研究除了將年度的因素考慮主題分析之外,利用GHSOM除了提供之前自我組織映射圖(Self-Organizing Map, SOM)能重要研究主題上的論文分布情形與主題彼此之間的視覺化,它更改進了SOM的二個缺點,無法自動確定大小的映射圖及表達出資料之間的階層性。本研究利用利他相關文獻作範例,分析結果除了提供這些學科的所關注的主題及學科之間交互關係,有助我們快速掌握研究文獻所呈現的研究概況。
The purpose of this study was to propose a hierarchical, annual research topic maps using Growing Hierarchical Self-Organizing Map (GHSOM), an improved Self-Organizing Map (SOM) algorithm. Unlike SOM, GHSOM can implement a dynamic architecture automatically and represents the hierarchical relations of results. The topic map illustrated the delicate intertwining of topics of annual research and provided a more explicit illustration of the concepts within each subject area. After taking up one example of altruism, this study suggests that topic map may disclose some important annual research topics from a whole bunch of data.