Linked Open Data 2019
A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Big Data and Augmented Intelligence".
Deadline for manuscript submissions: closed (30 November 2019) | Viewed by 305
Special Issue Editors
Interests: database; data integration; data fusion; linked open data; big data
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent years, there has been an increase in open data government initiatives promoting the idea that particular data produced by public administrations (such as public spending, health care, education, etc.) should be freely published. Governments intend to support participatory citizen governance by allowing citizens and businesses to make information-based decisions. In order to improve the use and usefulness of open data, it is important that data consumers are able to link data objects and concepts within and between data sets. For this reason, linked data developed in a large number of data sets with open access from different domains is leading to linked open data.
Linked open data requires dealing with various technical problems, such as how to perform information extraction, transformation, and integration, how to manage data quality, and how to model metadata. Furthermore, linked open data requires an appropriate compromise between contrasting factors, such as data privacy and data usefulness.
Therefore, the purpose of this Special Issue is to publish high-quality research, from academic and industrial stakeholders, for disseminating innovative methodologies, concepts, and technologies that contribute to the exploitation of linked open data to address technical and societal issues.
Topics of interest include, but are not limited to, the following topics:
- Semantic for linked open data extraction, transformation, and integration
- Querying linked open data
- Visualization and interaction for ontologies and linked open data
- Linked open data discovery and exploration
- Knowledge graphs and deep semantics
- Machine learning and data mining methods for linked open data
- Data mining and knowledge discovery in linked open data
- Natural language processing and artificial intelligence techniques for linked open data
- Scalability issues: Linked open data and ontologies for big data
- Linked open data engineering (development, alignment, evolution, provenance, etc.)
- Applications areas: Policymaking; economics; healthcare; business; industry
- Data quality issues in linked open data
Prof. Domenico Beneventano
Dr. Giovanni Simonini
Guest Editors
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Keywords
- linked open data
- data quality
- data integration
- big data
- ontologies
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