[public-lod2] CFP: 2nd Workshop on Linked Data Qquality at ESWC #LDQ2015

[Amrapali J Zaveri]

LDQ 2015 CALL FOR PAPERS

2nd Workshop on Linked Data Quality
co-located with ESWC 2015, Portorož, Slovenia
June 1, 2015
http://ldq.semanticmultimedia.org/ <http://ldq.semanticmultimedia.org/>

/*News flash: Invited talk by Prof.Dr.Felix Naumann on "Brave new data, 
revisited"*//*
*/http://ldq.semanticmultimedia.org/program/keynote_felix_naumann 
<http://ldq.semanticmultimedia.org/program/keynote_felix_naumann>

*Important Dates*
  * Submission of research papers: March 16, 2015
  * Notification of paper acceptance: April 9, 2015
  * Submission of camera-ready papers: April 30, 2015

Since the start of the Linked Open Data (LOD) Cloud, we have seen an 
unprecedented volume of structured data published on the web, in most 
cases as RDF and Linked (Open) Data. The integration across this LOD 
Cloud, however, is hampered by the ‘publish first, refine later’ 
philosophy. This is due to various quality problems existing in the 
published data such as incompleteness, inconsistency, 
incomprehensibility, etc. These problems affect every application 
domain, be it scientific (e.g., life science, environment), 
governmental, or industrial applications.

We see linked datasets originating from crowdsourced content like 
Wikipedia and OpenStreetMap such as DBpedia and LinkedGeoData and also 
from highly curated sources e.g. from the library domain. Quality is 
defined as “fitness for use”, thus DBpedia currently can be appropriate 
for a simple end-user application but could never be used in the medical 
domain for treatment decisions. However, quality is a key to the success 
of the data web and a major barrier for further industry adoption.

Despite the quality in Linked Data being an essential concept, few 
efforts are currently available to standardize how data quality tracking 
and assurance should be implemented. Particularly in Linked Data, 
ensuring data quality is a challenge as it involves a set of 
autonomously evolving data sources. Additionally, detecting the quality 
of datasets available and making the information explicit is yet another 
challenge. This includes the (semi-)automatic identification of 
problems. Moreover, none of the current approaches uses the assessment 
to ultimately improve the quality of the underlying dataset.

The goal of the Workshop on Linked Data Quality is to raise the 
awareness of quality issues in Linked Data and to promote approaches to 
assess, monitor, maintain and improve Linked Data quality.

The workshop*topics*include, but are not limited to:
  * Concepts
  *  - Quality modeling vocabularies
  * Quality assessment
  *  - Methodologies
  *  - Frameworks for quality testing and evaluation
  *  - Inconsistency detection
  *  - Tools/Data validators
  * Quality improvement
  *  - Refinement techniques for Linked Datasets
  *  - Linked Data cleansing
  *  - Error correction
  *  - Tools
  * Quality of ontologies
  * Reputation and trustworthiness of web resources
  * Best practices for Linked Data management
  * User experience, empirical studies

*Submission guidelines*
We seek novel technical research papers in the context of Linked Data 
Quality with a length of up to 8 pages (long) and 4 pages (short) 
papers. Papers should be submitted in PDF format. Other supplementary 
formats (e.g. html) are also accepted but a pdf version is required. 
Paper submissions must be formatted in the style of the Springer 
Publications format for Lecture Notes in Computer Science (LNCS). Please 
submit your paper via EasyChair at 
https://easychair.org/conferences/?conf=ldq2015 
<https://easychair.org/conferences/?conf=ldq2015>. Submissions that do 
not comply with the formatting of LNCS or that exceed the page limit 
will be rejected without review. We note that the author list does not 
need to be anonymized, as we do not have a double-blind review process 
in place. Submissions will be peer reviewed by three independent 
reviewers. Accepted papers have to be presented at the workshop.

*Organizing Committee*
  * Anisa Rula – University of Milano-Bicocca, IT
  * Amrapali Zaveri – AKSW, University of Leipzig, DE
  * Magnus Knuth – Hasso Plattner Institute, University of Potsdam, DE
  * Dimitris Kontokostas – AKSW, University of Leipzig, DE