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) | Viewed by 12619
Special Issue Editors
2. 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, Collections and Topics in MDPI journals
Interests: big and open data; data mining; machine learning; privacy and security by design; privacy and security engineering; risk management
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 submissions that pass pre-check are 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 1600 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.
