Laurens Rietveld

LOD Laundromat: A Uniform Way of Publishing Other People's Dirty Data

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.

YASGUI: Not Just Another SPARQL Client

This paper introduces YASGUI, a user-friendly SPARQL client. We compare YASGUI with other SPARQL clients, and show the added value and ease of integrating Web APIs, services, and new technologies such as HTML5. Finally, we discuss some of the challenges we encountered in using these technologies for a building robust and feature rich web application.

Linked Data-as-a-Service: The Semantic Web Redeployed

Ad-hoc querying is crucial to access information from Linked Data, yet publishing queryable RDF datasets on the Web is not a trivial exercise. The most compelling argument to support this claim is that the Web contains hundreds of thousands of data documents, while only 260 queryable SPARQL endpoints are provided. Even worse, the SPARQL endpoints we do have are often unstable, may not comply with the standards, and may differ in supported features. In other words, hosting data online is easy, but publishing Linked Data via a queryable API such as SPARQL appears to be too difficult. As a consequence, in practice, there is no single uniform way to query the LOD Cloud today. In this paper, we therefore combine a large-scale Linked Data publication project LOD Laundromat with a low-cost server-side interface Triple Pattern Fragments, in order to bridge the gap between the Web of downloadable data documents and the Web of live queryable data. The result is ai¾?repeatable, low-cost, open-source data publication process. To demonstrate its applicability, we made over 650,000 data documents available as datai¾?APIs, consisting of 30i¾?billion i¾?triples.

LOD Lab: Experiments at LOD Scale

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 command-line. 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.

The YASGUI family of SPARQL clients1

The size and complexity of the Semantic Web and its technology stack makes it difficult to query. Access to Linked Data could be greatly facilitated if it were supported by a tool with a strong focus on usability. In this paper we present the YASGUI family of SPARQL clients, a continuation of the YASGUI tool introduced more than two years ago. The YASGUI family of SPARQL clients enables publishers to improve ease of access for their SPARQL endpoints, and gives consumers of Linked Data a robust, feature-rich and user friendly SPARQL editor. We show that the YASGUI family had significant impact on the landscape of Linked Data management: YASGUI components are integrated in state-of-the-art triple-stores and Linked Data applications, and used as front-end by a large number of Linked Data publishers. Additionally, we show that the YASGUI web service - which provides access to any SPARQL endpoint - has a large and growing user base amongst Linked Data consumers.

The health care and life sciences community profile for dataset descriptions

Access to consistent, high-quality metadata is critical to finding, understanding, and reusing scientific data. However, while there are many relevant vocabularies for the annotation of a dataset, none sufficiently captures all the necessary metadata. This prevents uniform indexing and querying of dataset repositories. Towards providing a practical guide for producing a high quality description of biomedical datasets, the W3C Semantic Web for Health Care and the Life Sciences Interest Group (HCLSIG) identified Resource Description Framework (RDF) vocabularies that could be used to specify common metadata elements and their value sets. The resulting guideline covers elements of description, identification, attribution, versioning, provenance, and content summarization. This guideline reuses existing vocabularies, and is intended to meet key functional requirements including indexing, discovery, exchange, query, and retrieval of datasets, thereby enabling the publication of FAIR data. The resulting metadata profile is generic and could be used by other domains with an interest in providing machine readable descriptions of versioned datasets.

LOD Laundromat: Why the Semantic Web Needs Centralization (Even If We Don't Like It)

LOD Laundromat poses a centralized solution for todays Semantic Web problems. This approach adheres more closely to the original vision of a Web of Data, providing uniform access to a large and ever-increasing subcollection of the LOD Cloud.

Hubble: Linked Data Hub for Clinical Decision Support

The AERS datasets is one of the few remaining, large publicly available medical data sets that until now have not been published as Linked Data. It is uniquely positioned amidst other medical datasets. This paper describes the Hubble prototype system for clinical decision support that demonstrates the speed, ease and flexibility of producing and using a Linked Data version of the AERS dataset for clinical practice and research.

Meta-data for a lot of LOD

This paper introduces the LOD Laundromat meta-dataset, a continuously updated RDF meta-dataset that describes the documents crawled, cleaned and (re)published by the LOD Laundromat. This meta-dataset of over 110 million triples contains structural information for more than 650,000 documents (and growing). Dataset meta-data is often not provided alongside published data, it is incomplete or it is incomparable given the way they were generated. The LOD Laundromat meta-dataset provides a wide range of structural dataset properties, such as the number of triples in LOD Laundromat documents, the average degree in documents, and the distinct number of Blank Nodes, Literals and IRIs. This makes it a particularly useful dataset for data comparison and analytics, as well as for the global study of the Web of Data. This paper presents the dataset, its requirements, and its impact.

YASGUI: Feeling the Pulse of Linked Data

Existing studies of Linked Data focus on the availability of data rather than its use in practice. The number of query logs available is very much restricted to a small number of datasets. This paper proposes to track Linked Data usage at the client side. We use YASGUI, a feature rich web-based query editor, as a measuring device for interactions with the Linked Data Cloud. It enables us to determine what part of the Linked Data Cloud is actually used, what part is open or closed, the efficiency and complexity of queries, and how these results relate to commonly used dataset statistics.