Special Issue "Advances in Knowledge Graph and Data Science"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information and Communications Technology".

Deadline for manuscript submissions: closed (30 October 2019).

Special Issue Editors

Prof. Dr. Haklae Kim
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Guest Editor
Department of Library and Information Science, Chungang University, Seoul 06974, Korea
Interests: knowledge graph; data science; data-driven artificial intelligence
Dr. Jangwon Gim
E-Mail
Guest Editor
Department of SW Convergence Engineering, Kunsan National University, Gunsan 54150, Korea
Interests: knowledge graph; IoT; data science; artificial intelligence
Prof. Dr. Yuchul Jung
E-Mail Website
Guest Editor
Department of Computer Engineering, Kumoh National Institute of Technology (KIT), Gumi 39177, Korea
Interests: information retrieval and natural language processing; SW-based robotics; public health; artificial intelligence (AI); machine learning stuffs

Special Issue Information

Dear Colleagues,

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
Guest Editors

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.

Keywords

  • Knowledge population using Open Data
  • Knowledge graph enrichment using data science
  • Knowledge construction based on NLP technologies
  • Advanced techniques for knowledge discovery

Published Papers (2 papers)

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Research

Open AccessArticle
A Mapping Approach to Identify Player Types for Game Recommendations
Information 2019, 10(12), 379; https://doi.org/10.3390/info10120379 - 02 Dec 2019
Abstract
As the size of the domestic and international gaming industry gradually grows, various games are undergoing rapid development cycles to compete in the current market. However, selecting and recommending suitable games for users continues to be a challenging problem. Although game recommendation systems [...] Read more.
As the size of the domestic and international gaming industry gradually grows, various games are undergoing rapid development cycles to compete in the current market. However, selecting and recommending suitable games for users continues to be a challenging problem. Although game recommendation systems based on the prior gaming experience of users exist, they are limited owing to the cold start problem. Unlike existing approaches, the current study addressed existing problems by identifying the personality of the user through a personality diagnostic test and mapping the personality to the player type. In addition, an Android app-based prototype was developed that recommends games by mapping tag information about the user's personality and the game. A set of user experiments were conducted to verify the feasibility of the proposed mapping model and the recommendation prototype. Full article
(This article belongs to the Special Issue Advances in Knowledge Graph and Data Science)
Open AccessArticle
A Study on Trend Analysis of Applicants Based on Patent Classification Systems
Information 2019, 10(12), 364; https://doi.org/10.3390/info10120364 - 23 Nov 2019
Abstract
In recent times, with the development of science and technology, new technologies have been rapidly emerging, and innovators are making efforts to acquire intellectual property rights to preserve their competitive advantage as well as to enhance innovative competitiveness. As a result, the number [...] Read more.
In recent times, with the development of science and technology, new technologies have been rapidly emerging, and innovators are making efforts to acquire intellectual property rights to preserve their competitive advantage as well as to enhance innovative competitiveness. As a result, the number of patents being acquired increases exponentially every year, and the social and economic ripple effects of developed technologies are also increasing. Now, innovators are focusing on evaluating existing technologies to develop more valuable ones. However, existing patent analysis studies mainly focus on discovering core technologies amongst the technologies derived from patents or analyzing trend changes for specific techniques; the analysis of innovators who develop such core technologies is insufficient. In this paper, we propose a model for analyzing the technical inventions of applicants based on patent classification systems such as international patent classification (IPC) and cooperative patent classification (CPC). Through the proposed model, the common invention patterns of applicants are extracted and used to analyze their technical inventions. The proposed model shows that patent classification systems can be used to extract the trends in applicants’ technological inventions and to track changes in their innovative patterns. Full article
(This article belongs to the Special Issue Advances in Knowledge Graph and Data Science)
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