With increasing amount of information (video, text) being available today, it has become non-trivial to develop techniques to categorize documents into contextually meaningful classes. The information as available in the documents is composed of sequence of events termed as patterns. It is evident to know the important trends as observed from patterns that are emerging over a specific time period and space. For identifying the patterns, we must focus on semantic meaning of documents. Tracing such patterns in videos or texts manually is a time-consuming, cumbersome or an impossible task. So, in this paper we have devised an unsupervised trend discovery approach that detects and correlates event patterns from videos temporally as well as spatially. We begin by building our own document collection on the basis of contextual meaning of documents. This helps in associating an input video with another video or text documents on the basis of their semantic meaning. This approach helps in accumulating variety of information that is scattered over the web thus providing relatively complete information about the video. The highly correlated words are grouped in a topic using Latent Dirichlet Allocation (LDA). To identify topics an E-MOWL based ontology is used. This event ontology helps in discovering associations and relations between the various events. With this kind of representation, the users can infer different concepts as emerged over time. For identifying the various spatial patterns that exist corresponding to an event in a document, we have utilized geographic ontology (Geoontology). We establish validity of our approach using experimental results. © 2014 IEEE.