SemStats 2014 - Second International Workshop on Semantic Statistics

The goal of this workshop is to explore and strengthen the relationship between the Semantic Web and statistical communities, to provide better access to the data held by statistical offices. It will focus on ways in which statisticians can use Semantic Web technologies and standards in order to formalize, publish, document and link their data and metadata. It follows the 1st Semantic Statistics workshop held at ISWC 2013 (SemStats 2013) that was a big success attracting more than 50 participants all along the day.
The statistical community shows more and more interest in the Semantic Web. In particular, initiatives have been launched to develop semantic vocabularies representing statistical classifications and discovery metadata. Tools are also being created by statistical organizations to support the publication of dimensional data conforming to the Data Cube W3C Recommendation. But statisticians see challenges in the Semantic Web: how can data and concepts be linked in a statistically rigorous fashion? How can we avoid fuzzy semantics leading to wrong analyses? How can we preserve data confidentiality?
The workshop will also cover the question of how to apply statistical methods or treatments to linked data, and how to develop new methods and tools for this purpose. Except for visualisation techniques and tools, this question is relatively unexplored, but the subject will obviously grow in importance in the near future.
Motivation
There is a growing interest regarding linked data and the Semantic Web in the statistical community. A large amount of statistical data from international and national agencies has already been published on the web of data, for example Census data from the U.S., Spain or France amongst others. In most cases, though, this publication is done by people exterior to the statistical office (see also http://datahub.io/dataset/istat-immigration, http://270a.info/ or http://eurostat.linked-statistics.org/), which raises issues such as long-term URI persistence, institutional commitment and data maintenance.
Statistical organisations are also interested in how Semantic Web might make it simpler for analysts to use well described statistical data in conjunction with other forms of data (eg geospatial information, scientific data, “big data” from various sources) which is expressed semantically. The ability to bring together diverse types of data in this way should enable new insights on multifaceted issues.
Statistical organizations also possess an important corpus of structural metadata such as concept schemes, thesauri, code lists and classifications. Some of those are already available as linked data, generally in SKOS format (e.g. FAO’s Agrovoc or UN’s COFOG). Semantic web standards useful for the statisticians have now arrived at maturity. The best examples are the W3C Data Cube, DCAT and ADMS vocabularies. The statistical community is also working on the definition of more specialized vocabularies, especially under the umbrella of the DDI Alliance. For example, XKOS extends SKOS for the representation of statistical classifications, and Disco defines a vocabulary for data documentation and discovery. The Visual Analytics Vocabulary is a first step towards semantic descriptions for user interface components developed to visualize Linked Statistical Data which can lead to increased linked data consumption and accessibility. We are now at the tipping point where the statistical and the Semantic Web communities have to formally exchange in order to share experiences and tools and think ahead regarding the upcoming challenges.
Statisticians have a long-going culture of data integrity, quality and documentation. They have developed industrialized data production and publication processes, and they care about data confidentiality and more generally how data can be used.
The web of data will benefit in getting rich data published by professional and trustworthy data providers. It is also important that metadata maintained by statistical offices like concept schemes of economic or societal terms, statistical classifications, well-known codes, etc., are available as linked data, because they are of good quality, well-maintained, and they constitute a corpus to which a lot of other data can refer to.
It seems that after a period where the aim was to publish as many triples as possible, the focus of the Semantic Web community is now shifting to having a better quality of data and metadata, more coherent vocabularies (see the LOV initiative), good and documented naming patterns, etc. This workshop aims to contribute in these longer term problems in order to have a significant impact.
The statistics community faces sometimes challenges when trying to adopt Semantic Web technologies, in particular:
difficulty to create and publish linked data: this can be alleviated by providing methods, tools, lessons learned and best practices, by publicizing successful examples and by providing support.
difficulty to see the purpose of publishing linked data: we must develop end-user tools leveraging statistical linked data, provide convincing examples of real use in applications or mashups, so that the end-user value of statistical linked data and metadata appears more clearly.
difficulty to use external linked data in their daily activity: it is important to develop statistical methods and tools especially tailored for linked data, so that statisticians can get accustomed to using them and get convinced of their specific utility.
To conclude, statisticians know how misleading it can be to exploit semantic connections without carefully considering and weighing information about the quality of these connections, the validity of inferences, etc. A challenge for them is to determine, to ensure and to inform consumers about the quality of semantic connections which may be used to support analysis in some circumstances but not others. The workshop will enable participants to discuss these very important issues.
Topics
The workshop will address topics related to statistics and linked data. This includes but is not limited to:
How to publish linked statistics?
What are the relevant vocabularies for the publication of statistical data?
What are the relevant vocabularies for the publication of statistical metadata (code lists and classifications, descriptive metadata, provenance and quality information, etc.)?
What are the existing tools? Can the usual statistical software packages (e.g. R, SAS, Stata) do the job?
How do we include linked data production and publication in the data lifecycle?
How do we establish, document and share best practices?
How to use linked data for statistics?
Where and how can we find statistics data: data catalogues, dataset descriptions, data discovery?
How do we assess data quality (collection methodology, traceability, etc.)?
How can we perform data reconciliation, ontology matching and instance matching with statistical data?
How can we apply statistical processes on linked data: data analysis, descriptive statistics, estimation, correction?
How to intuitively represent statistical linked data: visual analytics, results of data mining?