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).
We invite submissions in the areas and intersections of Data Science and Knowledge Graph, potential topics include but are not limited to the following:
* Paper submission: April 1, 2019 * Paper acceptance notifications: April 28, 2019 * Paper camera ready: May 15, 2019
Because we are looking to promote discussion about an emerging area, we encourage authors to submit various types:
Submissions should be made to DSKG2019 on EasyChair submission page.
Submitted papers should conform to the Springer LNCS style and should describe, in English, original work that has not been published or submitted for publication elsewhere.
Selected papers will be invited to submit an extended full paper for peer-reviewed publication in special issues.
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