On measuring the lattice of commonalities among several linked datasets

Abstract

A big number of datasets has been published according to the principles of Linked Data and this number keeps increasing. Although the ultimate objective is linking and integration, it is not currently evident how connected the current LOD cloud is. Measurements (and indexes) that involve more than two datasets are not available although they are important: (a) for obtaining complete information about one particular URI (or set of URIs) with provenance (b) for aiding dataset discovery and selection, (c) for assessing the connectivity between any set of datasets for quality checking and for monitoring their evolution over time, (d) for constructing visualizations that provide more informative overviews. Since it would be prohibitively expensive to perform all these measurements in a naïve way, in this paper we introduce indexes (and their construction algorithms) that can speedup such tasks. In brief, we introduce (i) a namespace-based prefix index, (ii) a sameAs catalog for computing the symmetric and transitive closure of the owl:sameAs relationships encountered in the datasets, (iii) a semantics-aware element index (that exploits the aforementioned indexes), and finally (iv) two lattice-based incremental algorithms for speeding up the computation of the intersection of URIs of any set of datasets. We discuss the speedup obtained by the introduced indexes and algorithms through comparative results and finally we report measurements about connectivity of the LOD cloud that have never been carried out so far.

LODsyndesis: Global Scale Knowledge Services

“…The methods and the construction algorithms of the above indexes can be found in [5][6][7], whereas all the offered services, which depend on the aforementioned indexes are described in Section 4.…”

“…The constructed semantically enriched indexes are given as input for performing connectivity analytics. We have performed measurements about the commonalities (i.e., intersection) among any combination of datasets, for several measurement types, which are the following: number of common entities, common properties, common classes, common literals and common facts (or triples) [5][6][7]. An example can be seen in the lower right part of Figure 2, where we can observe the number of common entities among any combination of the four datasets of our running example.…”

“…If we take the sum of their score in the directCounts list, i.e., the score of "3,4" is 2, and the score of "1,2,3,4" is 1, we will find that these two datasets contain three common entities. For performing the measurements fast, for any combination of datasets, we have proposed two different incremental lattice-based algorithms, which can compute the commonalities between millions of subsets in less than 3 s. All the technical details about the aforementioned algorithms can be found in [5][6][7].…”

“…For this reason, i.e., assisting the process of semantic integration of data at large scale, we have designed and developed novel indexes, methods and tools [5][6][7]. The current suite of services and tools that have been developed are known as "LODsyndesis" (http://www.ics.forth.gr/isl/LODsyndesis).…”

“…An overview of the available LODsyndesis-based services for these tasks can be seen in Figure 1. With respect to the work that have been made [5][6][7], in this paper:…”