YASGUI: Not Just Another SPARQL Client. In

Abstract. 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

Frank: The LOD Cloud at Your Fingertips

Abstract. Large-scale, algorithmic access to LOD Cloud data has been hampered by the absence of queryable endpoints for many datasets, a plethora of serialization formats, and an abundance of idiosyncrasies such as syntax errors. As of late, very large-scale -hundreds of thousands of document, tens of billions of triplesaccess to RDF data has become possible thanks to the LOD Laundromat Web Service. In this paper we showcase Frank, a command-line interface to a very large collection of standards-compliant, real-world RDF data that can be used to run Semantic Web experiments and stress-test Linked Data applications

Enhancing the Usefulness of Open Governmental Data with Linked Data Viewing Techniques

Open Governmental Data publishing has had mixed success. While many governmental bodies are publishing an increasing number of datasets online, the potential usefulness is rather low. This paper describes action research conducted within the context of the Dutch Cadastre’s open data platform. We start by observing contemporary (Dutch) Open Data platforms and observe that dataset reuse is not always realized. We introduce Linked Open Data, which promises to deliver solutions to the lack of Open Data reuse. In the process of implementing Linked Data in practice, we observe that users face a knowledge and skill and that contemporary Linked Open Data tooling is often unable to properly advertise the usefulness of datasets to potential users, thereby hampering reuse. We therefore develop four components for Linked Data viewing to enhance the current situation, making it easier to observe what a dataset is about and which potential use cases it could serve

Adaptive Low-level Storage of Very Large Knowledge Graphs

The increasing availability and usage of Knowledge Graphs (KGs) on the Web calls for scalable and general-purpose solutions to store this type of data structures. We propose Trident, a novel storage architecture for very large KGs on centralized systems. Trident uses several interlinked data structures to provide fast access to nodes and edges, with the physical storage changing depending on the topology of the graph to reduce the memory footprint. In contrast to single architectures designed for single tasks, our approach offers an interface with few low-level and general-purpose primitives that can be used to implement tasks like SPARQL query answering, reasoning, or graph analytics. Our experiments show that Trident can handle graphs with 10^11 edges using inexpensive hardware, delivering competitive performance on multiple workloads.Comment: Accepted WWW 202

Supplemental Information 2: Example dataset description

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

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

The YASGUI family of SPARQL clients

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

Frank: The LOD cloud at your fingertips?

Large-scale, algorithmic access to LOD Cloud data has been hampered by the absence of queryable endpoints for many datasets, a plethora of serialization formats, and an abundance of idiosyncrasies such as syntax errors. As of late, very large-scale - hundreds of thousands of document, tens of billions of triples - access to RDF data has become possible thanks to the LOD Laundromat Web Service. In this paper we showcase Frank, a command-line interface to a very large collection of standards-compliant, real-world RDF data that can be used to run Semantic Web experiments and stress-test Linked Data applications

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 re- sults of SemanticWeb evaluations are less generalizable than they should and—this paper argues—can be. This paper describes LOD Lab: a fun- damentally different evaluation paradigm that makes algorithmic evalu- ation 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 supportsWeb-scale evaluations to be run from the command-line.We il- lustrate the viability of the LOD Lab approach by rerunning experiments from three recent SemanticWeb 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 in- teresting research questions as to how algorithmic performance relates to (structural) properties of the data.

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