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.
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.
@cite_19 @cite_14 @cite_6 @cite_20
92788038 244060230 2568135881 2132573925
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. This paper studies the compressibility of RDF data sets. We show that big RDF data sets are highly compressible due to the structure of RDF graphs (power law), organization of URIs and RDF syntax verbosity. We present basic approaches to compress RDF data and test them with three well-known, real-world RDF data sets. With the proliferation of the RDF data format, engines for RDF query processing are faced with very large graphs that contain hundreds of millions of RDF triples. This paper addresses the resulting scalability problems. Recent prior work along these lines has focused on indexing and other physical-design issues. The current paper focuses on join processing, as the fine-grained and schema-relaxed use of RDF often entails star- and chain-shaped join queries with many input streams from index scans. We present two contributions for scalable join processing. First, we develop very light-weight methods for sideways information passing between separate joins at query run-time, to provide highly effective filters on the input streams of joins. Second, we improve previously proposed algorithms for join-order optimization by more accurate selectivity estimations for very large RDF graphs. Experimental studies with several RDF datasets, including the UniProt collection, demonstrate the performance gains of our approach, outperforming the previously fastest systems by more than an order of magnitude. Semi-structured data models like the Resource Description Framework (RDF), naturally allow for modeling the same real-world entity in various ways. For example, different RDF vocabularies enable the definition of various RDF graphs representing the same drug in Bio2RDF or Drugbank. Albeit semantically equivalent, these RDF graphs may be syntactically different, i.e., they have distinctive graph structure or entity identifiers and properties. Existing data-driven integration approaches only consider syntactic matching criteria or similarity measures to solve the problem of integrating RDF graphs. However, syntactic-based approaches are unable to semantically integrate heterogeneous RDF graphs. We devise SJoin, a semantic similarity join operator to solve the problem of matching semantically equivalent RDF graphs, i.e., syntactically different graphs corresponding to the same real-world entity. Two physical implementations are proposed for SJoin which follow blocking or non-blocking data processing strategies, i.e., RDF graphs can be merged in a batch or incrementally. We empirically evaluate the effectiveness and efficiency of the SJoin physical operators with respect to baseline similarity join algorithms. Experimental results suggest that SJoin outperforms baseline approaches, i.e., non-blocking SJoin incrementally produces results faster, while the blocking SJoin accurately matches all semantically equivalent RDF graphs.