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:
- Knowledge Population using Open (Government) Data
- Semantic Knowledge Integration for Open (Government) Data
- Various Analysis Efforts for Open (Government) Data Quality Management
- Use Cases of Government Open (Government) Data Visualization
- Advanced Techniques of Search and Knowledge Discovery for (Government) Open Data Portals
- Applications of Knowledge Graphs and Open (Government) Data
- Various NLP based Approaches to the Construction and Querying of Knowledge Graphs
- Knowledge Graph Expansion and Enrichment
- Open Sources (or tools) for Knowledge Graph Management
- Quality Measurement of Knowledge Graphs
- Knowledge Representation for Domain-specific Data
* Paper submission: April 1, 2019
* Paper acceptance notifications: April 28, 2019
* Paper camera ready: May 15, 2019Because we are looking to promote discussion about an emerging area, we encourage authors to submit various types:
- Works-In-Progress: To facilitate sharing of thought-provoking ideas and high-potential though preliminary research, authors are welcome to make submissions describing early-stage, in-progress, and/or exploratory work.
- Demonstrations: Demos should be submitted as video (e.g. MP4). Demos should be submitted with an abstract or short paper. Submitted demo videos should be no longer than 5 minutes.
- Traditional Papers: short paper (2 pages) or long paper (5 to 10 pages)
- Posters
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.
- Brahmananda Sapkota, Samsung Electronics, Co. Ltd.
- Simon Scerri, Fraunhofer IAIS, University of Bonn
- Jungyeon Yang, Samsung Electronics, Co. Ltd.
- To be continued ..
- Haklae Kim, Chungang University
- Jangwon Gim, Kunsan National University
- Yuchul Jung, Kumoh National Institute of Technology
- Dongjun Suh, Kyungpook National University
- Minjung Lee, Sejong Cyber University
- Jiseong Son, Korea Institute of Science and Technology Information
If you have any questions, please feel free to send an email to [email protected]