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Special Issue "Advances in Knowledge Graph and Data Science"
Deadline for manuscript submissions: closed (30 October 2019).
Interests: knowledge graph; data science; data-driven artificial intelligence
Interests: knowledge graph; IoT; data science; artificial intelligence
Interests: information retrieval and natural language processing; SW-based robotics; public health; artificial intelligence (AI); machine learning stuffs
A knowledge graph is a large network of entities, including their semantic types, properties, and the relationships between them. 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 knowledge graphs widely to their real-world services. Obtaining a primary data source is critical to constructing a knowledge graph, since building new knowledge from scratch is by no means a trivial task. As we have already experienced, Wikipedia as an online encyclopedia in an open-data form, has been widely used for constructing new knowledge across a variety of domains. Recently, significant amounts of data have been 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. Furthermore, various methods and technologies in the field of data science play a critical role to analyzing large-scale data and to constructing a knowledge graph.
DSKG2019 (International Workshop on Data Science and Knowledge Graph) aims to share and discuss knowledge graph techniques based on open data both in 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).
Prof. Dr. Haklae Kim
Dr. Jangwon Gim
Prof. Dr. Yuchul Jung
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Knowledge population using Open Data
- Knowledge graph enrichment using data science
- Knowledge construction based on NLP technologies
- Advanced techniques for knowledge discovery