Characterising RDF data sets

Abstract

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.

RDF-TR: Exploiting structural redundancies to boost RDF compression

“…It is nonetheless true that, given the descriptive character of RDF, there exist predicate repetitions when describing resources of the same nature (e.g., among people). Although the number of predicate combinations (referred to as predicate families) used for subject descriptions theoretically grows with the number of predicates, the number of such combinations is bounded, even in datasets with a light schema [15]. In the following, we formalize the concept of predicate family based on the notion of predicate lists [15].…”

“…For example, as previously explained, it would be uncommon to find ''clint@eastwood.org'' as a value for a film duration, or ''Dead Man Walking'' as the family name of a person. In fact, it is usual that object values are related to a single predicate [15].…”

A Software Framework and Datasets for the Analysis of Graph Measures on RDF Graphs

“…The literature has found that values in the range of 2 < α < 3 are typical in many real-world networks [15]. The scale-free behaviour also applies to some datasets and measures of RDF datasets [6,8]. However, to reason about whether a distribution follows a power-law can be technically challenging [1], and computing the exponent α, that falls into a certain range of values, is not sufficient.…”

“…Examples of statistics are dataset size, property and vocabulary usage, data types used or average length of string literals. In terms of the topology of RDF graphs, previous works report on network measures mainly focusing on in-and out-degree distributions, reciprocity, and path lengths [2,8,9,21]. Nonetheless, the results of these studies are limited to a small fraction of the RDF datasets currently available.…”

LOD-a-lot

“…Relatively li le is known about the structural properties of the Semantic Web. Early work in this area has observed the presence of power-law distributions and other network-based features, such as clustering coe cient and path lengths, over individual datasets of (up to) millions of triples [4,9,7]. It is not yet known whether the structural properties of the LOD Cloud are the same as the structural properties of individual datasets.…”