It would be great to have an article from the cultural heritage 
perspective. Let me know if I can help (editing, etc.)

kc


-------- Forwarded Message --------
Subject: 	[CFP] Semantic Web Journal - Special Issue on Quality
Management of Semantic Web Assets (Data, Services and Systems)
Resent-Date: 	Mon, 10 Aug 2015 06:28:29 +0000
Resent-From: 	[log in to unmask]
Date: 	Mon, 10 Aug 2015 09:10:21 +0300
From: 	Jürgen Umbrich <[log in to unmask]>
Reply-To: 	Jürgen Umbrich <[log in to unmask]>
To: 	[log in to unmask]



*CFP: Semantic Web Journal - Special Issue on Quality Management of
Semantic Web Assets (Data, Services and Systems):*
http://www.semantic-web-journal.net/blog/call-papers-special-issue-quality-management-semantic-web-assets-data-services-and-systems 

<http://www.semantic-web-journal.net/blog/call-papers-special-issue-quality-management-semantic-web-assets-data-services-and-systems>


     Submission guidelines

*Deadline:October 31, 2015*

Submissions shall be made through the Semantic Web journal website at
http://www.semantic-web-journal.net
<http://www.semantic-web-journal.net/>. Prospective authors must take
notice of the submission guidelines posted at
http://www.semantic-web-journal.net/authors
<http://www.semantic-web-journal.net/authors>. Note that you need to
request an account on the website for submitting a paper. Please
indicate in the cover letter that it is for the Special Issue on Quality
Management of Semantic Web Assets (Data, Services and Systems).

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.


     Guest editors

   * Amrapali Zaveri, University of Leipzig, AKSW Group, Germany
   * Dimitris Kontokostas, University of Leipzig, AKSW Group, Germany
   * Sebastian Hellmann, University of Leipzig, AKSW Group, Germany
   * Jürgen Umbrich, Vienna University of Economics and Business, Austria

*Overview and Topics*

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.


     Topics of Interest

We welcome original high quality submissions on (but are not restricted
to) the following topics:

   * Methodologies and frameworks to plan, control, assure or improve the
     quality of Semantic Web Assets
   * Quality exploration and analysis interfaces
   * Quality monitoring
   * Developing, deploying and managing quality service ecosystems
   * Assessing the quality evolution of Semantic Web Assets
   * Large-scale quality assessment of structured datasets
   * Crowdsourcing data quality assessment
   * Quality assessment leveraging background knowledge
   * Use-case driven quality management
   * Evaluation of trustworthiness of data
   * Web Data and LOD quality benchmarks
   * Data Quality improvement methods and frameworks, e.g., linkage,
     alignment, cleaning, enrichment, correctness
   * Service/system quality improvement methods and frameworks
   * Managing sustainability issues in services
   * Guarantee of service (availability, performance)
   * Systems for transparent management of open data