Time series from textual instructions for causal relations discovery (Causal relations dataset)

One aspect of ontology learning methods is the discovery of relations in textual data. One kind of such relations are causal relations. Our aim is to discover causations described in texts such as recipes and manuals. There is a lot of research on causal relations discovery that is based on grammatical patterns. These patterns are, however, rarely discovered in textual instructions (such as recipes) with short and simple sentence structure. Therefore we use time series to discover causal relations. To do that, each word of interest in the text is converted into time series that represent how often and in which time stamp this word appears in the text. Then a time series analysis can be applied to discover causal relations.