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In the past, approaches to mining spatial and spatio-temporal data for interesting patterns have mainly concentrated on data obtained through observations and simulations where positions of objects, such as areas, vehicles, or persons, are collected over time. In the past couple of years, however, new datasets have been built by automatically extracting facts, as subject-predicate-object triples, from semi structured information sources such as Wikipedia. Recently some approaches, for example, in the context of YAGO2, have extended such facts by adding temporal and spatial information. The presence of such new data sources gives rise to new approaches for discovering spatio-temporal patterns. In this paper, we present a framework in support of the discovery of interesting spatio-temporal patterns from knowledge base datasets. Different from traditional approaches to mining spatio-temporal data, we focus on mining patterns at different levels of granularity by exploiting concept hierarchies, which are a key ingredient in knowledge bases. We introduce a pattern specification language and outline an algorithmic approach to efficiently determine complex patterns. We demonstrate the utility of our framework using two different real-world datasets from YAGO2 and the Website eventful.com.