Trend Template: Mining Trends With a Semi-formal Trend Model

Predictions of uprising or falling trends are helpful in different scenarios in which users have to deal with huge amount of information in a timely manner,such as during financial analysis. This temporal aspect in various cases of data analysis requires novel data mining techniques. Assuming that a given set of data, e.g. web news, contains information about a potential trend, e.g. financial crisis, it is possible to apply statistical or probabilistic methods in order to find out more information about this trend. However, we argue that in order to understand the context, the structure, and explanation of a trend, it is necessary to take a knowledge-based approach. In our study we define trend mining and propose the application of an ontology-based trend model for mining trends from textual data. We introduce the preliminary definition of trend mining as well as two components of our trend model: the trend template and the trend ontology. Furthermore, we discuss the results of our experiments with trend ontology on the test corpus of German web news. We show that our trend mining approach is relevant for different scenarios in ubiquitous data mining.