英文摘要 |
In this research we propose a novel approach to develop a platform for discovering supply-chain competitive analysis on an ontology-based web-mining technique. Also, by integrating a text mining approach with a XML document technique, in the developed platform we provide a way to allow businesses tackle difficulties in knowledge management for the supply-chain related information. To testify the developed web-mining algorithm, in this research a corpus associated with industrial information collected from specific news web sites (e.g. CNA News), with the RosettaNet standard framework, is employed as the major information source for conducting system implementation and case study. By applying the developed web-mining algorithm, in this work we attempt to extract concepts and knowledge from a huge semi-structured and unstructured HTML-document collections. The extracted concepts and knowledge can then be used to produce metadata and ontology to describe the contents in the original web documents. As such, the original web documents can be transformed into XML documents and stored in the XML document database based upon the ontology based "knowledge template". The research applies a text-mining approach to automating the construction and maintenance of a concept-hierarchy, in order to establish a XML document database based on the extracted metadata and ontology. The approach for knowledge extraction in this research is mainly using a Web-content mining method. That is, the existing WWW pages can be analyzed to generate a set of metadata to describe their content and produce an ontology for the XML document database through a text-mining technique, incorporated with a neural-net machine learning method for implementation. |