The availability of videos has grown rapidly in recent years. Finding and browsing relevant information to be automatically extracted from videos is not an easy task, but today it is an indispensable feature due to the immense number of digital products available. In this paper, we present a system which provides a process to automatically extract information from videos. We describe a system solution that uses a re-trained OpenNLP model to locate all the places and famous people included in a specific video. The system obtains information from the Google Knowledge Graph related to relevant named entities such as places or famous people. In this paper we will also present the Automatic Georeferencing Video (AGV) system developed by RAI (Radiotelevisione italiana, which is the national public broadcasting company of Italy, owned by the Ministry of Economy and Finance) Teche for the European Project “La Città Educante” (The Educating City: teaching and learning processes in cross-media ecosystem) Our system contributes to The Educating City project by providing the technological environment to create statistical models for automatic named entity recognition (NER), and has been implemented in the field of education, in Italian initially. The system has been applied to the learning challenges facing the world of educational media and has demonstrated how beneficial combining topical news content with scientific content can be in education.

近年来，视频的可用性增长迅速。查找和浏览要从视频中自动提取的相关信息并非易事，但如今，由于可用的数字产品数量众多，它已成为不可或缺的功能。在本文中，我们提出了一个系统，该系统提供了一个从视频中自动提取信息的过程。我们描述了一个系统解决方案，它使用重新训练的 OpenNLP 模型来定位特定视频中包含的所有地点和名人。系统从 Google 知识图谱中获取与相关命名实体（例如地点或名人）相关的信息。在本文中，我们还将介绍由意大利国家公共广播公司 RAI (Radiotelevisione italiana) 开发的自动地理配准视频 (AGV) 系统，经济和财政部所有） Teche 欧洲项目“La Città Educante”（教育城市：跨媒体生态系统中的教学和学习过程）我们的系统通过提供技术环境来创建统计数据，为教育城市项目做出贡献自动命名实体识别 (NER) 模型，并已在教育领域实施，最初是在意大利语中。该系统已应用于教育媒体世界面临的学习挑战，并展示了将专题新闻内容与科学内容相结合对教育的益处。跨媒体生态系统中的教学和学习过程）我们的系统通过提供技术环境来创建自动命名实体识别 (NER) 的统计模型，为教育城市项目做出了贡献，并已在教育领域实施，最初是在意大利语中。该系统已应用于教育媒体世界面临的学习挑战，并展示了将专题新闻内容与科学内容相结合对教育的益处。跨媒体生态系统中的教学和学习过程）我们的系统通过提供技术环境来创建自动命名实体识别 (NER) 的统计模型，为教育城市项目做出了贡献，并已在教育领域实施，最初是在意大利语中。该系统已应用于教育媒体世界面临的学习挑战，并展示了将专题新闻内容与科学内容相结合对教育的益处。