Abstract
With the growing complexity of document contents and the significant increase of domain knowledge, it is difficult for knowledge receivers to understand the specific domain knowledge. However, the traditional knowledge extraction schemes usually provide complete documents to the knowledge receivers and much time is required for the knowledge receivers to acquire domain knowledge. The concept of component-based knowledge is to divide the documents into several knowledge components corresponding more specific domains and can be used to reduce the time required for the knowledge receivers to search the specific domain knowledge. Moreover, since the figures and tables in a document usually contain the important implicit knowledge expressed within the document, the aim of this research is to extract the knowledge components form the documents (e.g., the industry yearbooks) on the basis of figures and tables. In this research, a Knowledge Component Extraction (KCE) model with two algorithms namely Keyword Mapping Algorithm (KMA) and Sentence Mapping Algorithm (SMA) is developed. In order to demonstrate applicability of the proposed mothodology, a web-based knowledge component extraction system is also established based on the proposed model. Furthermore, the Taiwan Logistics Yearbooks are applied as examples to evaluate the proposed model. The verification results show that the developed system is a high-performance knowledge component extraction system. As a whole, this research provides an approach for knowledge receivers to efficiently and accurately acquire the domain knowledge.