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The 5th Workshop on Big Scholarly Data
A workshop of KDD 2018 (The 24rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining)
London, United Kingdom, 19 - 23 August, 2018
The number of scholarly documents produced by academics, researchers, and practitioners worldwide is increasing at an unprecedented speed. The term big scholarly data is coined for this rapidly growing scholarly source of information. Many large collections of scholarly data including digital libraries, search engines, repositories, knowledge bases, Wikipedia, and the Web have already covered millions of journal articles, conference proceedings, degree theses, books, patents, technical reports, tutorials, course materials, etc. For instance, the Microsoft Academic Graph contains scientific publication records, citation relationships between those publications, as well as authors, institutions, journals, conferences, and fields of study. The DBLP bibliography now lists more than 5000 conference and workshop series, as well as more than 1500 journals in computer science, which involve more than 4 million publications by more than 2 million authors.
Big scholarly data bring about new opportunities and challenges with respect to knowledge discovery, data mining, science of science, and education. It is imperative and vital for scholars to drive their knowledge towards the innovative generation of values from big scholarly data. New knowledge can be extracted by analyzing and mining big scholarly data to, e.g., better understand research dynamics, scientific collaboration and success, identify new directions of research, assess the quality of science, and enable personalized teaching and learning. To achieve these goals, however, a lot of challenges facing big scholarly data acquisition, storage, management, processing and usage must be addressed.
Following the success of the previous four editions, the BigScholar 2018 workshop aims at bringing together academics and practitioners from diverse fields to share ideas and experience with management, analysis, mining, and applications of big scholarly data. The goal is to contribute to the birth of a community having a shared interest around big scholarly data and exploring it using knowledge discovery, data science and analytics, network science, and other appropriate technologies. The workshop will be a full-day workshop. The format of the workshop will include 4-5 invited talks (including keynotes), research and position paper presentations, and one discussion panel (TBD). The workshop will be held on 20 August 2018 in London, UK, in conjunction with the 24rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2018).
Topics of interest include (but not limited to):
- New approaches to search, crawling and integration of scholarly data from various data sources
- Methods for storing, indexing, and query processing for big scholarly data
- Practices for scholarly data management and sharing
- Big scholarly data analysis, mining, and visualization
- Network science for scholarly data analytics
- Graph and text mining in big scholarly data
- Measuring the impact of publications, funding, courses, individuals, teams, etc.
- Computational behavioural sciences in research and education
- Academic social network analysis and mining
- Scholarly recommendation
- Understanding and predicting success in research and education
- Design of next generation platforms and systems for research and education
- Novel services and applications for research and education
Submissions due: May 15, 2018
Notification: June 8, 2018
Workshop date: August 20, 2018
Authors are invited to submit papers of various types, including (but not limited to):
- Unpublished research/full papers
- Work-in-progress papers
- Position papers
- Relevant work that has been previously published
- Relevant technical reports
All submitted papers must:
* be written in English;
* contain author names, affiliations, and email addresses;
* be formatted properly, and of any style (though the standard double-column ACM Proceedings Style is recommended);
* be of an appropriate length (Note: Extremely long papers will be rejected without review, though there is no limit on the page length of any submission);
* be in PDF (make sure that the PDF can be viewed on any platform).
All submissions will be peer-reviewed by members of the Program Committee. There is no formal proceedings for this workshop. The accepted papers will be posted on the workshop website and will not be considered archival for resubmission purposes. For each accepted paper, at least one author must attend the workshop and present the paper.
Please submit your paper here:
Feng Xia, Dalian University of Technology
Huan Liu, Arizona State University
Irwin King, The Chinese University of Hong Kong
Kuansan Wang, Microsoft Research
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