2013 international workshop on computational scientometrics: theory and applications

Abstract

The field of Scientometrics is concerned with the analysis of science and scientific research. As science advances, scientists around the world continue to produce large numbers of research articles, which provide the technological basis for worldwide collection, sharing, and dissemination of scientific discoveries. Research ideas are generally developed based on high quality citations. Understanding how research ideas emerge, evolve, or disappear as a topic, what is a good measure of quality of published works, what are the most promising areas of research, how authors connect and influence each other, who are the experts in a field, what works are similar, and who funds a particular research topic are some of the major foci of the rapidly emerging field of Scientometrics. Digital libraries and other databases that store research articles have become a medium for answering such questions. Citation analysis is used to mine large publication graphs in order to extract patterns in the data (e.g., citations per article) that can help measure the quality of a journal. Scientometrics, on the other hand, is used to mine graphs that link together multiple types of entities: authors, publications, conference venues, journals, institutions, etc., in order to assess the quality of science and answer complex questions such as those listed above. Tools such as maps of science that are built from digital libraries, allow different categories of users to satisfy various needs, e.g., help researchers to easily access research results, identify relevant funding opportunities, and find collaborators. Moreover, the recent developments in data mining, machine learning, natural language processing, and information retrieval makes it possible to transform the way we analyze research publications, funded proposals, patents, etc., on a web-wide scale.