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Researchers worldwide are currently producing more and more scholarly data of various types such as papers, books, patents, etc. Such data are big data by nature. For example, the DBLP Computer Science Bibliography and the Microsoft Academic Graph/API (research.microsoft.com/mag) provide bibliographic information on major computer science journals and proceedings. DBLP and MAG index more than 3 and 100 million articles, respectively, with records containing title, pages, years and authors¨ information, etc. Concurrently, scholars are associated with various academic activities such as conferences, workshops, congresses, peer review and so on. Such scenarios have motivated us to also explore the Web of Scholars in the context of big scholarly data on a global scale. It is imperative and vital for researchers to drive their knowledge towards the innovative generation of values from Big Scholarly Data. The emerging worldwide Web of Scholars demands a re-evaluation of existing techniques, such as data mining, recommender systems and social network analysis. Furthermore, there is the demand for novel ways of developing algorithms, methods and techniques to foster the analysis and interpretation of social environments such as academic collaboration networks.

In this workshop, we will explore the most promising areas of research in big scholarly data, with focus on major foci of the rapidly emerging field of the Web of Scholars. This workshop also seeks to answer noteworthy research questions such as:
- How to model the Web of scholars?
- How to connect scholars on the Web?
- How to measure impact of publications, researchers, groups, or institutions?

- How to visualize Big Scholarly Data for insights and analytics?
- How to utilize the Web of Scholars to improve the way research is being done?

Researchers are welcome to submit highly interesting and quality papers that address these questions above and other topics below which may include, but are not limited to:
- Academic social network analysis
- Scientific measurement
- Scholarly data management
- Digital infrastructures for accessing scholarly data
- Methods and tools for analyzing and visualizing big scholarly data
- Indexing, searching, and mining scholarly data
- Connecting scholars using a Web approach
- Paradigms to promote scientific collaboration
- Scientific trends prediction
- Web tools and techniques for big scholarly data
- Systems, platforms, and services exploring the Web of Scholars
- Applications and use cases of big scholarly data



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