An intelligent agent application for buyers in electronic commerce

One of the mostly used areas of information agents is electronic commerce. At business to customer commerce which is one of the parts of electronic commerce, web pages including information about properties of products such as price, color, size, etc. enable customers to be more conscious to shop. Because of browsing thousands of web pages will be impossible for customers, intelligent information agents play essential role for collecting, processing data in Internet and producing meaningful results for customers.This thesis includes a new information extraction algorithm which is based on frequencies of sequential word groups, and design and application of an intelligent agent that acquire knowledge from Internet to automatically build a knowledge base.To evaluate the algorithm, an intelligent agent which is called FERBot (Feature Extractor Recognizer Bot) is designed. FERbot collects the labeled or unlabeled information from structured or unstructured web pages to inform customers about the features of products and value range of these features for reasonable and cost efficient shopping.This new algorithm is an alternative for the current information extraction algorithms that extract information from web. It plays an important role for text miners and natural language processors to process the extracted information.