This workshop aims to discuss key issues and practices of Web Science. Web Science is concerned with the full scope of socio-technical relationships that are engaged in the World Wide Web, and is thus inherently interdisciplinary. We need to understand “science of decentralized information systems” and analyze its usefulness and benefit to people. In order to understand phenomena of Web evolution, interplay between aspects of computer science in different fields (e.g. computer and information science, sociology, law, communication, and psychology, etc.) is required. The workshop will provide a cross-disciplinary forum for researchers to share their research efforts and ideas between industry and academia. In particular, we will discuss what the future of the Web Science might be in the light of the recent wave of Semantic Web and Social Web technologies and services. In addition, we will organize invited talks that will discuss the interdisciplinary challenges of Social Semantic Web. We solicit the following types of submissions:
Highly-innovative research contributions; new perspectives on semantic and social data mining, empirical studies for emerging trends and technologies, etc.
Position papers discussing the future of the Web Science
By bringing together representatives of academia and industry, the workshop is also a means for identifying new research problems and disseminating results of the research and practice.


Topics of Interest

The aim of this workshop is to cover all aspects that relate to research and practical issues of Web Science. Topics of interest include but are not limited to:

Semantic and Social Data
(Governmental) Linked Data
General ontologies for (open) linked data
Creating and combining social and linked data
Scalable solutions for linking linked data
Linked data analysis Entity disambiguation on large scale linked data
Enabling social trust using provenance
Visualizing semantic & linked data
The dark side of the Web (e.g. cybercrime and terrorism)
Trust and privacy Evolving technologies (e.g. search technologies)
Case studies of communities such as Twitter, Facebook as well as empirical findings in social semantic web
Mining collective and semantic-based social network
Mining semantics of social relationships
Integrating computational network analysis and semantic web techniques
Data Science including modeling, resolving, and querying the Web of Data
Data Science on Technology/Competitive/Collective Intelligence
Trends and Technologies on Data Science