Scholarly Big Data (SBD2013)
Academics and researchers worldwide continue to produce large numbers of scholarly documents including papers, books, technical reports, etc. and associated data such as tutorials, proposals, lab note books, and course materials. For example PubMed has over 20 million documents, 10 million unique names and 70 million name mentions. Google Scholar has many millions more it is believed. Understanding how at scale 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 Scholarly Big Data.
Digital libraries, repositories, databases, wikipedia, funding agencies and the web have become a medium for answering such questions. For example 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 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 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; and funding sources identify new directions of research and the impact of existing funding. 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.
The workshop aims at bringing together researchers with diverse interdisciplinary backgrounds interested in mining, managing and search scholarly big data. See the call for papers for more information.
|08h45-10h00||Keynote by Brewster Kahle, Internet Archive|
The Microsoft Academic Search Challenges at KDD Cup
Martine De Cock
Senjuti Basu Roy
Bibliometric-enhanced Retrieval Models for Big
Scholarly Information Systems
Academic Publishing as a Social Media Paradigm
Michael E. Payne
Linh B. Ngo
Amy W. Apon
|13h30-15h40||Discussion and breakout sessions|