We present a system that automatically recommends tags for YouTube videos solely based on their audiovisual content. We also propose a novel framework for unsupervised discovery of video categories that exploits knowledge mined from the World-Wide Web text documents/searches. First, video content to tag association is learned by training classifiers that map audiovisual content-based features from millions of videos on YouTube.com to existing uploader-supplied tags for these videos. When a new video is uploaded, the labels provided by these classifiers are used to automatically suggest tags deemed relevant to the video. Our system has learned a vocabulary of over 20,000 tags. Secondly, we mined large volumes of Web pages and search queries to discover a set of possible text entity categories and a set of associated is-A relationships that map individual text entities to categories. Finally, we apply these is-A relationships mined from web text on the tags learned from audiovisual content of videos to automatically synthesize a reliable set of categories most relevant to videos -- along with a mechanism to predict these categories for new uploads. We then present rigorous rating studies that establish that: (a) the average relevance of tags automatically recommended by our system matches the average relevance of the uploader-supplied tags at the same or better coverage and (b) the average precision@K of video categories discovered by our system is 70% with K=5.