It is widely accepted that proper data publishing is difficult. The majority of Linked Open Data (LOD) does not meet even a core set of data publishing guidelines. Moreover, datasets that are clean at creation, can get stains over time. As a result, the LOD cloud now contains a high level of dirty data that is difficult for humans to clean and for machines to process.Existing solutions for cleaning data (standards, guidelines, tools) are targeted towards human data creators, who can (and do) choose not to use them. This paper presents the LOD Laundromat which removes stains from data without any human intervention. This fully automated approach is able to make very large amounts of LOD more easily available for further processing right now.LOD Laundromat is not a new dataset, but rather a uniform point of entry to a collection of cleaned siblings of existing datasets. It provides researchers and application developers a wealth of data that is guaranteed to conform to a specified set of best practices, thereby greatly improving the chance of data actually being (re)used.

The owl:sameAs predicate is an essential ingredient of the Semantic Web architecture. It allows parties to independently mint names, while at the same time ensuring that these parties are able to understand each other's data. An online resource that collects all owl:sameAs statements on the Linked Open Data Cloud has therefore both practical impact (it helps data users and providers to find different names for the same entity) as well as analytical value (it reveals important aspects of the connectivity of the LOD Cloud). This paper presents sameAs.cc: the largest dataset of identity statements that has been gathered from the LOD Cloud to date. We describe an efficient approach for calculating and storing the full equivalence closure over this dataset. The dataset is published online, as well as a web service from which the data and its equivalence closure can be queried.

In the absence of a central naming authority on the Semantic Web, it is common for different datasets to refer to the same thing by different IRIs. Whenever multiple names are used to denote the same thing, owl:sameAs statements are needed in order to link the data and foster reuse. Studies that date back as far as 2009, have observed that the owl:sameAs property is sometimes used incorrectly. In this paper, we show how network metrics such as the community structure of the owl:sameAs graph can be used in order to detect such possibly erroneous statements. One benefit of the here presented approach is that it can be applied to the network of owl:sameAs links itself, and does not rely on any additional knowledge. In order to illustrate its ability to scale, the approach is evaluated on the largest collection of identity links to date, containing over 558M owl:sameAs links scraped from the LOD Cloud.

Abstract. LOD-a-lot democratizes access to the Linked Open Data (LOD) Cloud by serving more than 28 billion unique triples from 650K datasets over a single self-indexed file. This corpus can be queried online with a sustainable Linked Data Fragments interface, or downloaded and consumed locally: LOD-a-lot is easy to deploy and demands affordable resources (524 GB of disk space and 15.7 GB of RAM), enabling Webscale repeatable experimentation and research even by standard laptops.

Contemporary Semantic Web research is in the business of optimizing algorithms for only a handful of datasets such as DBpedia, BSBM, DBLP and only a few more. This means that current practice does not generally take the true variety of Linked Data into account. With hundreds of thousands of datasets out in the world today the results of Semantic Web evaluations are less generalizable than they should and -this paper argues -can be. This paper describes LOD Lab: a fundamentally different evaluation paradigm that makes algorithmic evaluation against hundreds of thousands of datasets the new norm. LOD Lab is implemented in terms of the existing LOD Laundromat architecture combined with the new open-source programming interface Frank that supports Web-scale evaluations to be run from the commandline. We illustrate the viability of the LOD Lab approach by rerunning experiments from three recent Semantic Web research publications and expect it will contribute to improving the quality and reproducibility of experimental work in the Semantic Web community. We show that simply rerunning existing experiments within this new evaluation paradigm brings up interesting research questions as to how algorithmic performance relates to (structural) properties of the data.