A Framework for Knowledge Classification and Related-Information Search

• Main Authors: Mei-Chu Huang, 黃美珠
• Other Authors: Yen-Teh Hsia
• Format: Others
• Language: zh-TW
• Published: 2001
• Online Access: http://ndltd.ncl.edu.tw/handle/20186921986239244071

碩士 === 中原大學 === 資訊工程研究所 === 89 === Today, most content-based search methods use keywords to search the information-items. Sometimes general classifications are also considered in the search. These search methods greatly depend on the use of keywords. They seldom do reasonable analysis of the real contents of the information-items. As a result, some of the information-items found will turn out to be irrelevant. In order that all information-items found are relevant, it is advisable that we first classify these information-items appropriately. This classification is necessarily knowledge-based. One way of doing this is to use our domain knowledge to construct a "knowledge tree", and then to categorize each information-item under its related categories. This in turn means that we need to analyze the contents of the information-items. Once we do so, the relatedness between any two information-items will be implicitly represented by the relatedness between their "immediate enclosing categories." In this thesis, we first turn our (domain-dependent) classification knowledge into a knowledge tree, and then we use this knowledge tree as a basis for classifying the information-items. Any information-item can be classified into multiple categories. This implements the idea of using multiple perspectives to view things. Since the relatedness of the information-items is implicitly expressed by the structure of the knowledge tree, information-items in the same "immediate closing category" are related by definition. To further calculate the relatedness between any two information-items, we also designed a set of computation rules. This allows us to quantify relatedness. To show how our approach works, we did two case studies. One is about the classification of (a small domain of) high tech news-items. The other is about the classification of museum items. Both studies demonstrate how related information-items can be found and suggested to the viewer of the current information item.