Special Issue "Linked Open Data"

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

Deadline for manuscript submissions: closed (1 July 2019).

Special Issue Editors

Prof. Dr. Sunil Choenni
E-Mail Website1 Website2
Guest Editor
• Professor at Rotterdam University of Applied Sciences (RUAS), Research Centre Creating 010, 3015 GG Rotterdam, The Netherlands
• Head of Statistical Data and Policy Analysis Division (SIBa), Research and Documentation Centre (WODC), Ministry of Justice and Security, The Hague, The Netherlands
Interests: big and open data; privacy; e-government; artificial intelligence
Special Issues and Collections in MDPI journals
Dr. Mortaza S. Bargh
E-Mail Website1 Website2
Guest Editor
Researcher at Research and Documentation Center, Dutch Ministry of Justice and Security, The Hague, The Netherlands
Interests: big and open data; data mining; machine learning; privacy and security by design; privacy and security engineering; risk management
Dr. Susan Van den Braak
E-Mail Website
Guest Editor
Researcher at Research and Documentation Center, Dutch Ministry of Justice and Security, The Hague, The Netherlands
Interests: big and open data; e-government; artificial intelligence

Special Issue Information

Dear Colleagues,

To improve their transparency, accountability, and efficiency, governments seek to open their (public-funded) data sets—containing registration data, aggregated data, and research data—to the public proactively. In this way, governments intend to support participatory governance by citizens, to foster innovations and economic growth, and to enable citizens and businesses to make informed personal and business decisions. In order to enhance the use and usefulness of the opened data, it is important that data consumers are able to link data objects and concepts within and across datasets. For example, applying semantic web technologies (such as RDF and OWL), the field of linked data provides a framework for developing rich applications that query data and draw inferences by using well-defined vocabularies. In addition, data linkage through, for instance, syntactic attribute matching (i.e., via primary and secondary key attributes) or semantic matching can offer similar outcomes for an effective use of open government data. However, in order to make linked data and data linkage in open data settings a reality, it is important to deal with a number of data-related challenges, such as misunderstanding, interoperability, quality, and privacy, effectively. Ideally, the semantics of and the relationship among data objects and concepts should be clear and unambiguous in order to link them effectively and correctly. Moreover, linking open data should not impact citizens and individuals negatively, for instance, by violating their privacy or imposing unjustifiable discrimination.

Opening (linkable) data therefore requires addressing various technical issues, such as how to carry out information extraction, how to model uncertainty, how to deal with data quality, and how to model metadata. Moreover, opening (linkable) data requires making appropriate trade-offs between contending values, such as data privacy (representing the rights of individuals) and data utility (representing the rights of the society). While doing so, knowledge and insights available on expected threats, like privacy and misinterpretation issue, should be taken into account.

The aim of this Special Issue is to foster research on methodologies, concepts, and technologies that contribute to the exploitation of linked/linkable open data for addressing societal issues and creating added business values. We invite the research community and practitioners to present their innovative (applied) research results or novel applications of linked/linkable open data related, but not limited, to the following topics:

Applications areas:

  • E-learning;
  • Economics;
  • Insurance;
  • Policymaking;
  • Healthcare;
  • Business;
  • Industry.

Technical challenges:

  • Information extraction;
  • Ontology learning and topic modeling;
  • Interoperability of data sets;
  • Data visualization;
  • Metadata;
  • Data quality issues;
  • Scalability issues;
  • Noise reduction and data decontamination;
  • Lack of (enough) structure in data;
  • Semantics of data;
  • Querying open data;
  • Measurement models for open data (measuring the degree of openness, impact, etc.).

Nontechnical challenges:

  • Misinterpretation and misunderstanding;
  • Privacy breaches and personal data disclosures;
  • Ethical issues;
  • Discrimination;
  • Organizational aspects of data opening;
  • Tools and concepts for a proper interpretation of open data;
  • Dealing with legacy data;
  • Semi-openness;
  • Measurements issues of open data (measuring the degree of openness, impact, etc.);
  • Exploiting domain knowledge.

Note that the submitted work should be related to the general topic of linked/linkable open data in some way. In case of any doubt, please feel free to contact the editors.

Dr. Sunil Choenni
Dr. Mortaza S. Bargh
Dr. Susan van den Braak
Guest Editor

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 1400 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.

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
Interoperability Conflicts in Linked Open Statistical Data
Information 2019, 10(8), 249; https://0-doi-org.brum.beds.ac.uk/10.3390/info10080249 - 27 Jul 2019
Cited by 1 | Viewed by 1699
Abstract
An important part of Open Data is of a statistical nature and describes economic and social indicators monitoring population size, inflation, trade, and employment. Combining and analyzing Open Data from multiple datasets and sources enable the performance of advanced data analytics scenarios that [...] Read more.
An important part of Open Data is of a statistical nature and describes economic and social indicators monitoring population size, inflation, trade, and employment. Combining and analyzing Open Data from multiple datasets and sources enable the performance of advanced data analytics scenarios that could result in valuable services and data products. However, it is still difficult to discover and combine Open Statistical Data that reside in different data portals. Although Linked Open Statistical Data (LOSD) provide standards and approaches to facilitate combining statistics on the Web, various interoperability challenges still exist. In this paper, we propose an Interoperability Framework for LOSD, comprising definitions of LOSD interoperability conflicts as well as modelling practices currently used by six official open government data portals. Towards this end, we combine a top-down approach that studies interoperability conflicts in the literature with a bottom-up approach that studies the modelling practices of data portals. We define two types of LOSD schema-level conflicts, namely naming conflicts and structural conflicts. Naming conflicts result from using different URIs. Structural conflicts result from different practices of modelling the structure of data cubes. Only two out of the 19 conflicts are currently resolved and 11 can be resolved according to literature. Full article
(This article belongs to the Special Issue Linked Open Data)
Show Figures

Figure 1

Article
Beyond Open Data Hackathons: Exploring Digital Innovation Success
Information 2019, 10(7), 235; https://0-doi-org.brum.beds.ac.uk/10.3390/info10070235 - 09 Jul 2019
Cited by 3 | Viewed by 2198
Abstract
Previous researchers have examined the motivations of developers to participate in hackathons events and the challenges of open data hackathons, but limited studies have focused on the preparation and evaluation of these contests. Thus, the purpose of this paper is to examine factors [...] Read more.
Previous researchers have examined the motivations of developers to participate in hackathons events and the challenges of open data hackathons, but limited studies have focused on the preparation and evaluation of these contests. Thus, the purpose of this paper is to examine factors that lead to the effective implementation and success of open data hackathons and innovation contests. Six case studies of open data hackathons and innovation contests held between 2014 and 2018 in Thessaloniki were studied in order to identify the factors leading to the success of hackathon contests using criteria from the existing literature. The results show that the most significant factors were clear problem definition, mentors’ participation to the contest, level of support to participants by mentors in order to launch their applications to the market, jury members’ knowledge and experience, the entry requirements of the competition, and the participation of companies, data providers, and academics. Furthermore, organizers should take team members’ competences and skills, as well as the support of post-launch activities for applications, into consideration. This paper can be of interest to organizers of hackathon events because they could be knowledgeable about the factors that should take into consideration for the successful implementation of these events. Full article
(This article belongs to the Special Issue Linked Open Data)
Article
A Proximity-Based Semantic Enrichment Approach of Volunteered Geographic Information: A Study Case of Waste of Water
Information 2019, 10(7), 234; https://0-doi-org.brum.beds.ac.uk/10.3390/info10070234 - 08 Jul 2019
Cited by 1 | Viewed by 1465
Abstract
Volunteered geographic information (VGI) refers to geospatial data that is collected and/or shared voluntarily over the Internet. Its use, however, presents many limitations, such as data quality, difficulty in use and recovery. One alternative to improve its use is to use semantic enrichment, [...] Read more.
Volunteered geographic information (VGI) refers to geospatial data that is collected and/or shared voluntarily over the Internet. Its use, however, presents many limitations, such as data quality, difficulty in use and recovery. One alternative to improve its use is to use semantic enrichment, which is a process to assign semantic resources to metadata and data. This study proposes a VGI semantic enrichment method using linked data and thesaurus. The method has two stages, one automatic and one manual. The automatic stage links VGI contributions to places that are of interest to users. In the manual stage, a thesaurus in the hydric domain was built based on terms found in VGI. Finally, a process is proposed, which returns semantically similar VGI contributions based on queries made by users. To verify the viability of the proposed method, contributions from the VGI system Gota D’Água, related to water waste prevention, were used. Full article
(This article belongs to the Special Issue Linked Open Data)
Show Figures

Figure 1

Back to TopTop