Methods for Extracting Meta-Information from bibliographic databases

Due to intensive growth of the electronically available publications in the last few decades, bibliographic databases have become widespread. They cover a large variety of knowledge fields and provide a fast access to the wide variety of data. At the same time they contain a wealth of hidden knowledge that requires steps of extra processing in order to infer it. In this work we focus on extraction of such implicit (or meta) knowledge from the research bibliographic databases by looking at them from sociolinguistic, text mining and bibliometric perspectives. We choose the Digital Library and Bibliographic Database — DBLP as a testbed for our experiments. In the framework of the sociolinguistic analysis we build a statistical system for the language identification of personal names. We show also that extension of a purely statistical model with the co-authors network boosts the system’s performance. There are several premises motivating our work. For example, it has been shown that the geographical proximity influences research. Moreover, research is constantly evaluated on the national and international basis. To make these and similar investigations less laborious in terms of human effort, ability to automatically assign personal names to the appropriate language seems to be useful. In the text mining scenario, we perform a number of experiments that focus on topic identification and ranking. While our topic detection approach remains generic and can be used for any kind of textual data, the topic ranking metrics are built upon the information provided by the bibliographic databases. With respect to the topic ranking, our study aims at finding the ways of different topic ordering depending on the question that has