CoCoE stands for Complexity, Coherence and Entropy, and presents an extensible methodology for empirical analysis of Linked Open Data (i.e., RDF graphs). CoCoE can offer answers to questions like: Is dataset A better than B for knowledge discovery since it is more complex and informative?, Is dataset X better than Y for simple value lookups due its flatter structure?, etc. In order to address such questions, we introduce a set of well-founded measures based on complementary notions from distributional semantics, network analysis and information theory. These measures are part of a specific implementation of the CoCoE methodology that is available for download. Last but not least, we illustrate CoCoE by its application to selected biomedical RDF datasets.
@cite_4: The development of Semantic Web (RDF) brings new requirements for data analytics tools and methods, going beyond querying to semantics-rich analytics through warehouse-style tools. In this work, we fully redesign, from the bottom up, core data analytics concepts and tools in the context of RDF data, leading to the first complete formal framework for warehouse-style RDF analytics. Notably, we define i) analytical schemas tailored to heterogeneous, semantics-rich RDF graph, ii) analytical queries which (beyond relational cubes) allow flexible querying of the data and the schema as well as powerful aggregation and iii) OLAP-style operations. Experiments on a fully-implemented platform demonstrate the practical interest of our approach.
@cite_1: 
@cite_6: One of the reasons for the slow adoption of SPARQL is the complexity in query formulation due to data diversity. The principal barrier a user faces when trying to formulate a query is that he generally has no information about the underlying structure and vocabulary of the data. In this paper, we address this problem at the maximum scale we can think of: providing assistance in formulating SPARQL queries over the entire Sindice data collection - 15 billion triples and counting coming from more than 300K datasets. We present a method to help users in formulating complex SPARQL queries across multiple heterogeneous data sources. Even if the structure and vocabulary of the data sources are unknown to the user, the user is able to quickly and easily formulate his queries. Our method is based on a summary of the data graph and assists the user during an interactive query formulation by recommending possible structural query elements.
@cite_11: Although the cloud of Linked Open Data has been growing continuously for several years, little is known about the particular features of linked data usage. Motivating why it is important to understand the usage of Linked Data, we describe typical linked data usage scenarios and contrast the so derived requirement with conventional server access analysis. Then, we report on usage patterns found through an indepth analysis of access logs of four popular LOD datasets. Eventually, based on the usage patterns we found in the analysis, we propose metrics for assessing Linked Data usage from the human and the machine perspective, taking into account dierent agent types and resource representations.
The most relevant tools and approaches for RDF data analysis are @cite_6 @cite_1 @cite_11 @cite_4 . Perhaps closest to our work is @cite_1 that computes a set of statistics and histograms for a given RDF dataset. The statistics are, however, concerned mostly about distributions of statements, instances and explicit statement patterns. This may be useful for tasks like SPARQL query optimisation, but cannot directly answer questions that motivate our work. Graph summaries @cite_6 propose high-level abstractions of RDF data intended to facilitate formulation of SPARQL queries, which is orthogonal to our approach aimed at quantitative characteristics of the data itself. Usage-based RDF data analysis @cite_11 provides insights into common patterns of utilising RDF data by agents, but does not offer means for actually analysing the data. Finally, the recent approach @cite_4 is useful for knowledge discovery in RDF data based on user-defined query patterns and analytical perspectives. Our approach complements @cite_4 by characterising application-independent features of RDF datasets taken as a whole.