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_19: In this paper, we describe a comprehensive analysis of graph-theoretical properties of online social networks based on the Friend-of-a-Friend (FOAF) ontology. Of particular interest for this work were properties related to the small-world phenomenon. More than 1.6 million of the FOAF documents collected on the Semantic Web met our requirements and were analyzed in depth. Most FOAF documents are created and published by social networking services, blog hosting services, or combinations of the two as a matter of routine; only a fractional amount are maintained by individuals. Although the FOAF ontology defines unique identifiers for persons in theory, retrieval and particularly fusion of personal information is difficult and error-prone in practice. Nevertheless, we identified the largest strongly connected components of various community networks based on FOAF documents and analyzed them in regard to the small-world phenomenon. Interestingly, all components examined exhibited a characteristic path length comparable to the smallest length achievable for a graph of the respective size, and the clustering coefficient was much greater than expected for an equivalent random graph; along with power law degree distributions, both are typical features of small-world graphs.
  @cite_14: We present Graphium Chrysalis, a tool to visualize the main graph invariants that characterize RDF graphs, i.e., graph properties that are independent of the graph representation such as, vertex and edge counts, in- and out-degree distribution, and in-coming and out-going (h )-index. Graph invariants characterize a graph and impact on the cost of the core graph-based tasks, e.g., graph traversal and sub-graph pattern matching, affecting time and space complexity of main RDF reasoning and query processing tasks. During the demonstration of Graphium Chrysalis, attendees will be able to observe and analyze the invariants that describe graphs of existing RDF benchmarks. Additionally, we will show the expressiveness power of state-of-the-art graph database engine APIs (e.g., Neo4j or Sparksee) (Sparksee was previously known as DEX), when main graph invariants are computed against RDF graphs.
  @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_20: Semantic Web languages are being used to represent, encode and exchange semantic data in many contexts beyond the Web – in databases, multiagent systems, mobile computing, and ad hoc networking environments. The core paradigm, however, remains what we call the Web aspect of the Semantic Web – its use by independent and distributed agents who publish and consume data on the World Wide Web. To better understand this central use case, we have harvested and analyzed a collection of Semantic Web documents from an estimated ten million available on the Web. Using a corpus of more than 1.7 million documents comprising over 300 million RDF triples, we describe a number of global metrics, properties and usage patterns. Most of the metrics, such as the size of Semantic Web documents and the use frequency of Semantic Web terms, were found to follow a power law distribution.
summary: In the area of structural network analysis, it is common to study the distribution of certain graph measures in order to characterize a graph. RDF datasets have also been subject to these studies. The study by @cite_20 reveals that the power-law distribution is prevalent across graph invariants in RDF graphs obtained from 1.7 million documents. Also, the small-world phenomenon, known from experiments on social networks were studied within the Semantic Web @cite_19 . More recently, Fern ' @cite_6 have studied the structural features of real-world RDF data. Fern ' also propose measures in terms of in- and -out degrees for subjects, objects, and predicates and analyze the structure of @math RDF graphs from different knowledge domains. Most of these works focus on studying different in- and out-degree distributions and are limited to a rather small collection of RDF datasets. Moreover, the work by @cite_14 analyze further relevant graph invariants in RDF graphs including @math index and reciprocity. The work by applied graph-based metrics on synthetic RDF datasets. Complementary to these works, we present an study on @math RDF datasets from the LOD Cloud and analyze their structure based on the average degree, @math -index, and powerlaw exponent.