Theme and Topics
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:
- Quality modeling vocabularies
- Quality assessment
- Frameworks for quality testing and evaluation
- Inconsistency detection
- Tools/Data validators
- Quality improvement
- Refinement techniques for Linked Datasets
- Linked Data cleansing
- Error correction
- Quality of ontologies
- Reputation and trustworthiness of web resources
- Best practices for Linked Data management
- User experience, empirical studies
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. Paper submissions should 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. 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 to be published in the proceedings. Proceedings will be published online at CEUR workshop proceedings series. The best papers accepted for this workshop will be included in the supplementary proceedings of ESWC 2015, which will appear in the Springer LNCS series.
All deadlines are, unless otherwise stated, at 23:59 Hawaii time.
- Submission of research papers
- Monday, March 16, 2015
March 6, 2015
- Notification of paper acceptance
- Wednesday, April 15, 2015
April 3, 2015
- Submission of camera-ready papers
- Thursday April 30, 2015
April 17, 2015
- Workshop date
- Monday, June 1, 2015 (Morning Session)
More details can be found on the workshop website: http://ldq.semanticmultimedia.org/