由於專家內隱知識的具體化一直都是個值得探討的議題，也因此一直以來有許多的研究朝此方向發展。透過正規概念分析法可將專家知識具體概念化後，利用知識地圖的方式來呈現專家知識的概念是如何相互繼承與關聯。於是，如何利用具體化後的知識亦形成另一個重要的議題，亦即一旦當專家知識過於複雜龐大時，即使正規概念分析法仍舊能將其結構化表示，然而卻難以簡潔地提供決策者進行判讀。另一方面，當有取得新進資訊時，龐大複雜的知識地圖中大部份的正規概念與其相關性明顯較低，以致於其知識地圖難以有效利用的問題更形嚴重。因此本研究將著手於透過已結構化的知識中找出與其相關的概念結構方式，以進一步來抽取出相關正規概念並進行重新組織以提供最相關也最簡潔的知識結構供決策者參考利用。

Formation of knowledge maps from experts’ implicit knowledge is an important issue. In order to improve the quality of decision and the sense process, there are many researches which focus on it. Formal concept analysis is an organized and systematical method for presenting experts’ implicit knowledge in a hierarchical and graphical way, revealing the inheritance relationships among concepts. The possibility of creating knowledge maps exists even when the experts’ knowledge is extremely complicated, but the problem relies on how to make the knowledge helpful to decision makers. Moreover, the integration of new information to existent knowledge maps is also an issue that needs to be addressed. This research proposes a method to perform object-concept matching and tries to extract essential information from concept maps to allow reorganization and simplification of a concept lattice with too many objects and attributes hinders human interpretation.

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