Deadline (only 1 month left):
Submissions shall be made
through the Semantic Web journal website at http://www.semantic-web-
Submissions are possible in the following categories: full research papers, application reports, reports on tools and systems, and case studies. While there is no upper limit, paper length must be justified by content.
The standardization and adoption of Semantic Web technologies has resulted in a variety of assets, including an unprecedented volume of data being semantically enriched and systems and services, which consume or publish this data. Although gathering, processing and publishing data is a step towards further adoption of Semantic Web, quality does not yet play a central role in these assets (e.g., data lifecycle, system/service development).
Quality management essentially refers to activities and tasks involved to guarantee a certain level of consistency and to meet the quality requirements for the assets. In general, quality management consists of the following four phases and components: (i) quality planning, (ii) quality control, (iii) quality assurance and (iv) quality improvement.
The quality planning phase in the Semantic Web typically involves the design of procedures, strategies and policies to support the management of the assets. The quality control and assurance components have their primary aim in preventing errors and to meet quality requirements pertaining to the Semantic Web standards. A core part for both components are quality assessment methods which provide the necessary input for the controlling and assurance tasks.
Quality assessment of Semantic Web Assets (data, services and systems), in particular, presents new challenges that were not handled before in other research areas. Thus, adopting existing approaches for data quality assessment is not a straightforward solution. These challenges are related to the openness of the Semantic Web, the diversity of the information and the unbounded, dynamic set of autonomous data sources, publishers and consumers (legal and software agents). Additionally, detecting the quality of available data sources and making the information explicit is yet another challenge. Moreover, noise in one data set, or missing links between different data sets, propagates throughout the Web of Data, and imposes great challenges on the data value chain.
In case of systems and services, different implementations follow the specifications for RDF and SPARQL to varying extents, or even propose and offer new, non-standardized extensions. This causes strong incompatibilities between systems, e.g., between the used SPARQL features in the query engines and support features in RDF stores. The potential heterogeneity and incompatibility poses several challenges for the quality assessments in and for such systems and services.
Eventually, quality improvement methods are used to further enhance the value of the Semantic Web Assets. One important step to improve the quality of data is identifying the root cause of the problem and then designing corresponding data improvement solutions. These solutions select the most effective and efficient strategies and related set of techniques and tools to improve quality. Quality improvement metrics for products and services entails understanding and improving operational processes and establishing valid and reliable service performance measures.
This Special Issue is addressed to those members of the community interested in providing novel methodologies or frameworks in managing, assessing, monitoring, maintaining and improving the quality of the Semantic Web data, services and systems and also introduce tools and user interfaces which can effectively assist in this management.
We welcome original high quality submissions on (but are not restricted to) the following topics: