Workshop 3. Tuesday, June 11th 14:00-17:30 (#101, 1F)
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/