Discovering the topics of a data source: a statistical approach

In this paper, we present a preliminary approach for automatically discovering the topics of a structured data source with respect to a reference ontology. Our technique relies on a signature, i.e., a weighted graph that summarizes the content of a source. Graph-based approaches have been already used in the literature for similar purposes. In these proposals, the weights are typically assigned using traditional information-theoretical quantities such as entropy and mutual information. Here, we propose a novel data-driven technique based on composite likelihood to estimate the weights and other main features of the graphs, making the resulting approach less sensitive to overfitting. By means of a comparison of signatures, we can easily discover the topic of a target data source with respect to a reference ontology. This task is provided by a matching algorithm that retrieves the elements common to both the graphs. To illustrate our approach, we discuss a preliminary evaluation in the form of running example.