Discovering the topics of a data source: A statistical approach?

A novel data-driven technique based on composite likelihood to estimate the weights and other main features of the graphs is proposed, making the resulting approach less sensitive to overfitting.

Abstract

Abstract. In this paper, we present a preliminary approach for automatically dis-covering the topics of a structured data source with respect to a reference ontol-ogy. 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 lit-erature for similar purposes. In these proposals, the weights are typically assigned using traditional information-theoretical quantities such as entropy and mutual in-formation. 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. 1