In this paper, some important features of a new approach to data summarization are introduced. Our model named SAINTETIQ produces summaries of groups of database records with different granularities. A summary is represented on each attribute by fuzzy sets associated to linguistic descriptors. One major feature of the SAINTETIQ system is the intensive use of background knowledge (BK) in the summarization process. BK is built a priori on each attribute. It supports both a translation step of descriptions of database tuples into a user-defined vocabulary, and a generalization step providing synthetic intents of summaries. Furthermore, the fuzzy set-based representation of summaries allows the system to improve robustness and accuracy of summary descriptions.
['Guillaume Raschia', 'Noureddine Mouaddib']
Using fuzzy labels as background knowledge for linguistic summarization of databases