Lecture Information Service based on Multiple Features Fusion

Abstract

Information service is always a hot topic especially when web is accessible anywhere. In university, lecture information is very import for students and teachers who want to take part in academic meetings. Therefore, lecture news extraction is an important and imperative task. Although many open information extraction methods have been proposed, but due to the highly heterogeneity of website, this task is still a challenge. In this manuscript, we propose a method based on fusing multiple features to locate lecture news in university web site. These features including the organization structure of lecture news catalog webpage, the visual similarity and the semantic of webpage. Additionally, this paper provide an information service based on a main content extraction algorithm for extracting lecture information. The stable and invariant features enable the propose method could adaptive to many kinds of campus website. The experiments conducted on 50 websites show the effectiveness and efficiencies of provided service.