Information service is always a hot topic especially when the Web is accessible anywhere. In university, lecture information is very important for students and teachers who want to take part in academic meetings. Therefore, lecture news extraction is an important and imperative task. Many open information extraction methods have been proposed, but due to the high heterogeneity of websites, this task is still a challenge. In this paper, we propose a method based on fusing multiple features to locate lecture news on the university website. These features include the linked relationship between parent webpage and child webpages, the visual similarity, and the semantics of webpages. Additionally, this paper provides an information service based on a main content extraction algorithm for extracting the lecture information. Stable and invariant features enable the proposed method to adapt to various kinds of campus websites. The experiments conducted on 50 websites show the effectiveness and efficiency of the provided service.

中文翻译：

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基于多特征融合的讲座信息服务

信息服务一直是一个热门话题，尤其是在任何地方都可以访问 Web 的情况下。在大学里，讲座信息对于想要参加学术会议的学生和教师来说非常重要。因此，讲座新闻提取是一项重要而紧迫的任务。已经提出了许多开放的信息提取方法，但是由于网站的高度异构性，这项任务仍然是一个挑战。在本文中，我们提出了一种基于融合多个特征的方法来定位大学网站上的讲座新闻。这些特征包括父网页和子网页之间的链接关系、视觉相似性以及网页的语义。此外，本文提供了一种基于主要内容提取算法的信息服务，用于提取讲座信息。稳定不变的特征使所提出的方法能够适应各种校园网站。在 50 个网站上进行的实验显示了所提供服务的有效性和效率。