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On multi-subjectivity in linguistic summarization of relational databases

Niewiadomski A., Superson I.

Journal of Theoretical and Applied Computer Science

2014

Vol. 8, nr 1

15--34

We focus on one of the most powerful computing methods for natural-language-driven representation of data, i.e. on Yager’s concept of a linguistic summary of a relational database (1982). In particular, we introduce an original extension of that concept: new forms of linguistic summaries. The new forms are named Multi-Subject linguistic summaries, because they are constructed to handle more than one set of subjects, represented by related sets of records/objects collected in a database, like ”cars, bicycles and motorbikes” (within vehicles), ”male and female” (within people), e.g. More boys than girls play football well. Thanks to that, the generated linguistic summaries – quasi-natural language sentences – are more interesting and human-oriented. Moreover, they can be applied together with the classic forms od summaries, to enrich naturality of comments/descriptions generated. Apart from traditional interpretions linguistic summaries in terms of fuzzy logic, we also introduce some higher-order fuzzy logic methods, to extend possibilities of representing too complex or too ill-defined linguistic terms used in generated messages. The new methods are applied to a computer system that generates natural language description of numeric data, that makes them possible to be clearly presented to an end-user.

Algorithm for generalization of action rules to summaries

Tzacheva A. A.

Control and Cybernetics

2010

Vol. 39, no 2

457-468

A knowledge discovery system is prone to yielding plenty of patterns, presented in the form of rules. Sifting through to identify useful and interesting patterns is a tedious and time consuming process. An important measure of interestingness is: whether or not the pattern can be used in the decision making process of a business to increase profit. Hence, actionable patterns, such as action rules, are desirable. Action rules may suggest actions to be taken based on the discovered knowledge. In this way contributing to business strategies and scientific research. The large amounts of knowledge in the form of rules presents a challenge of identifying the essence, the most important part, of high usability. We focus on decreasing the space of action rules through generalization. In this paper, we propose an improved method for discovering short descriptions of action rules. The new algorithm produces summaries by maximizing the diversity of rule pairs, and minimizing the cost of the suggested actions.