Workshop 1. Tuesday, June 11th 09:00-10:30, Room A (#101)
5th International Workshop on Knowledge Discovery on the Web (KDWEB 2019)
Knowledge Discovery is an interdisciplinary area focusing upon methodologies for identifying valid, novel, potentially useful and meaningful patterns from such data, and currently is widespread in numerous fields, including science, engineering, healthcare, business, and medicine. Recently, the rapid growth of social networks and online services entailed that Knowledge Discovery approaches focused on the World Wide Web (WWW), whose popular use as global information system led to a huge amount of digital data. KDWeb 2019 is focused on the field of Knowledge Discovery from digital data, with particular attention for Data Mining, Machine Learning, and Information Retrieval methods, systems, and applications.
KDWeb 2019 is aimed at providing a venue to researchers, scientists, students, and practitioners involved in the fields of Knowledge Discovery on Data Mining, Information Retrieval, and Semantic Web, for presenting and discussing novel and emerging ideas. KDWeb 2019 will contribute to discuss and compare suitable novel solutions based on intelligent techniques and applied in real-world applications.
Organizers: Giuliano Armano (University of Cagliari, Italy), Matteo Cristani (University of Verona, Italy)
Website: http://www.iascgroup.it/kdweb2019.html
Workshop 2. Tuesday, June 11th 11:00-12:30, Room A (#101)
2nd International Workshop on Maturity of Web Engineering Practices (MATWEP 2019)
MATWEP 2019 aims to be an interactive event where we invite submissions from academia and industry about experiences using techniques, methods, approaches that have specifically defined or adapted for the development of Web applications. The focus is as well on student activities as the use of Web engineering approaches in industrial or research projects. The main goal of this workshop is to offer a forum to exchange experiences and ideas related to maturity level of Web Engineering approaches in practice. Topics of interest include among others model-driven web engineering, agile development and user interface design of web applications. The workshop should provide examples how to provide bridges from the theory to the practice, showing problems in knowledge transfer as well as lessons learned. It should provide important feedback for improving Web engineering techniques, methods and approaches.
Organizers: José González Enríquez (University of Seville), Francisco José Domínguez Mayo (University of Seville), Nora Koch (IWT2 Research group), Esteban Morillo Baro (Servinform, S.A.)
Website: http://iwt2.org/matwep2019/
Workshop 3. Tuesday, June 11th 14:00-17:30, Room A (#101)
International Workshop on Data Science and Knowledge Graph (DSKG 2019)
A knowledge graph is large networks of entities, their semantic types, properties, and relationships between entities. It ultimately facilitates the creation of information necessary for machines to understand the world in the manner that humans do. Companies that aim to serve intelligent services such as Google, Microsoft, or IBM are applying the knowledge graph widely to its real-world services.
Obtaining a primary data source is critical to construct a knowledge graph, since building a new knowledge from scratch is not trivial. As we have already experienced, Wikipedia as open data has been widely used for constructing new knowledge across a variety of domains. Recently, significant amounts of data are published as open data in research, commercial and governments. These data can be a starting point for constructing a domain-specific knowledge graph through the interlinking of heterogeneous data.
This workshop aims to share and discuss about knowledge graph techniques based on open data both academia and industries. In particular, this workshop focuses on various use cases including data wrangling, data analysis, data visualization in the prospect of Data Science, and technical challenges to construct structured knowledge from large-scale raw data (focused on open data).
Organizers: Haklae Kim (Chungang University, South Korea), Jangwon Gim (Kunsan National University, South Korea), Yuchul Jung (Kumoh National Institute of Technology, South Korea), Dongjun Suh (Kyungpook National University, South Korea), Minjung Lee (Sejong Cyber University, South Korea), Jiseong Son (Korea Institute of Science and Technology Information, South Korea)
Website: https://datasciencekorea.github.io/
