text: As the availability and the inter-connectivity of RDF datasets grow, so does the necessity to understand the structure of the data. Understanding the topology of RDF graphs can guide and inform the development of, e.g. synthetic dataset generators, sampling methods, index structures, or query optimizers. In this work, we propose two resources: (i) a software framework (Resource URL of the framework: https: doi.org 10.5281 zenodo.2109469) able to acquire, prepare, and perform a graph-based analysis on the topology of large RDF graphs, and (ii) results on a graph-based analysis of 280 datasets (Resource URL of the datasets: https: doi.org 10.5281 zenodo.1214433) from the LOD Cloud with values for 28 graph measures computed with the framework. We present a preliminary analysis based on the proposed resources and point out implications for synthetic dataset generators. Finally, we identify a set of measures, that can be used to characterize graphs in the Semantic Web.
  @cite_18: The Linked Data initiative continues to grow making more datasets available; however, discovering the type of data contained in a dataset, its structure, and the vocabularies used still remains a challenge hindering the querying and reuse. VoID descriptions provide a starting point but a more detailed analysis is required to unveil the implicit vocabulary usage such as common data patterns. Such analysis helps the selection of datasets, the formulation of effective queries, or the identification of quality issues. Loupe is an online tool for inspecting datasets by looking at both implicit data patterns as well as explicit vocabulary definitions in data. This demo paper presents the dataset inspection capabilities of Loupe.
  @cite_9: Over the last years, the Semantic Web has been growing steadily. Today, we count more than 10,000 datasets made available online following Semantic Web standards. Nevertheless, many applications, such as data integration, search, and interlinking, may not take the full advantage of the data without having a priori statistical information about its internal structure and coverage. In fact, there are already a number of tools, which offer such statistics, providing basic information about RDF datasets and vocabularies. However, those usually show severe deficiencies in terms of performance once the dataset size grows beyond the capabilities of a single machine. In this paper, we introduce a software component for statistical calculations of large RDF datasets, which scales out to clusters of machines. More specifically, we describe the first distributed in-memory approach for computing 32 different statistical criteria for RDF datasets using Apache Spark. The preliminary results show that our distributed approach improves upon a previous centralized approach we compare against and provides approximately linear horizontal scale-up. The criteria are extensible beyond the 32 default criteria, is integrated into the larger SANSA framework and employed in at least four major usage scenarios beyond the SANSA community.
  @cite_6: The publication of semantic web data, commonly represented in Resource Description Framework (RDF), has experienced outstanding growth over the last few years. Data from all fields of knowledge are shared publicly and interconnected in active initiatives such as Linked Open Data. However, despite the increasing availability of applications managing large-scale RDF information such as RDF stores and reasoning tools, little attention has been given to the structural features emerging in real-world RDF data. Our work addresses this issue by proposing specific metrics to characterise RDF data. We specifically focus on revealing the redundancy of each data set, as well as common structural patterns. We evaluate the proposed metrics on several data sets, which cover a wide range of designs and models. Our findings provide a basis for more efficient RDF data structures, indexes and compressors.
  @cite_3: One of the major obstacles for a wider usage of web data is the difficulty to obtain a clear picture of the available datasets. In order to reuse, link, revise or query a dataset published on the Web it is important to know the structure, coverage and coherence of the data. In order to obtain such information we developed LODStats --- a statement-stream-based approach for gathering comprehensive statistics about datasets adhering to the Resource Description Framework (RDF). LODStats is based on the declarative description of statistical dataset characteristics. Its main advantages over other approaches are a smaller memory footprint and significantly better performance and scalability. We integrated LODStats with the CKAN dataset metadata registry and obtained a comprehensive picture of the current state of a significant part of the Data Web.
  @cite_0: Over a decade after RDF has been published as a W3C recommendation, publishing open and machine-readable content on the Web has recently received a lot more attention, including from corporate and governmental bodies; notably thanks to the Linked Open Data community, there now exists a rich vein of heterogeneous RDF data published on the Web (the so-called of Data") accessible to all. However, RDF publishers are prone to making errors which compromise the e ectiveness of applications leveraging the resulting data. In this paper, we discuss common errors in RDF publishing, their consequences for applications, along with possible publisher-oriented approaches to improve the quality of structured, machine-readable and open data on the Web.
  @cite_11: The central idea of Linked Data is that data publishers support applications in discovering and integrating data by complying to a set of best practices in the areas of linking, vocabulary usage, and metadata provision. In 2011, the State of the LOD Cloud report analyzed the adoption of these best practices by linked datasets within different topical domains. The report was based on information that was provided by the dataset publishers themselves via the datahub.io Linked Data catalog. In this paper, we revisit and update the findings of the 2011 State of the LOD Cloud report based on a crawl of the Web of Linked Data conducted in April 2014. We analyze how the adoption of the different best practices has changed and present an overview of the linkage relationships between datasets in the form of an updated LOD cloud diagram, this time not based on information from dataset providers, but on data that can actually be retrieved by a Linked Data crawler. Among others, we find that the number of linked datasets has approximately doubled between 2011 and 2014, that there is increased agreement on common vocabularies for describing certain types of entities, and that provenance and license metadata is still rarely provided by the data sources.
summary: This category includes studies about the general structure of RDF graphs at instance, schema, and metadata levels. @cite_11 present the status of RDF datasets in the LOD Cloud in terms of size, linking, vocabulary usage, and metadata. LODStats @cite_3 and the large-scale approach DistLODStats @cite_9 report on statistics about RDF datasets on the web, including number of triples, RDF terms, and properties per entity, and usage of vocabularies across datasets. Loupe @cite_18 is an online tool that reports on the usage of classes and properties in RDF datasets. Fern ' @cite_6 define measures to describe the relatedness between nodes and edges using subject-object, subject-predicate, and predicate-object ratios. @cite_0 study the distribution of RDF terms, classes, instances, and datatypes to measure the quality of public RDF data. In summary, the study of RDF-specific properties of publicly available RDF datasets have been extensively covered and is currently supported by online services and tools such as LODStats and Loupe. Therefore, in addition to these works, we focus on analyzing graph invariants in RDF datasets.