隨著網際網路的日益發達，有越來越多以文字為基礎的資訊出現，為了協助人們快速的搜尋到他們所需要的資訊，資訊擷取、文件分類、文件分群等技術被發展出來，這類技術有一大部分以所謂的向量模式為基礎，將文件或是查詢文字以單一文字為維度的向量加以表示，並以文字出現在文件或查詢文字中的頻率為維度值。這類以單一文字為維度的向量表示方式，忽略了那些可能有助於提升上述技術效果的文字間概念關係，例如同義字、上意字、下意字等。為了發展一套自動化的文字概念關係擷取技術，本研究應用型式概念分析，自動化的去針對一個文件集建立其文字關係架構，並發展一文件向量表示方式，應用所建立的文字關係架構將文件以概念為維度的向量加以表式，而為了評估其在相關應用上的效果，我們利用文件分群技術做為一個應用評估的方式。 With the continual improvement in internet-related technology, more and more information, especially text-based information, becomes available online. The implementation of most of these techniques draws upon Salton’s vector space model (VSM) in which documents or query strings are represented by vectors. Most implementations based on VSM employ the individual terms extracted from the documents or query strings as the dimensionalities of the vectors, and the frequency of terms appearing in the documents or query strings as the value of the dimensionalities. These implementations, or so-called bag-of-terms methods, ignore the conceptual relationships between terms such as synonyms, hypernyms and hyponyms that have been proven capable of improving the effectiveness of information retrieval, document classification and document clustering. To deal with the problem of an automatically- constructed thesaurus for a given document, in this study, we apply FCA to construct the term ontology to deal with the hierarchical conceptual relationships together with synonym-like relationships for the document set. We also develop a document representation method that applies ontology to represent documents by concept-based vectors. In order to evaluate the usability and effectiveness of our method, we make use of document clustering as the application used to evaluate the generated concept-based vectors.
