High Performance Methods for Linked Open Data Connectivity Analytics

Abstract

The main objective of Linked Data is linking and integration, and a major step for evaluating whether this target has been reached, is to find all the connections among the Linked Open Data (LOD) Cloud datasets. Connectivity among two or more datasets can be achieved through common Entities, Triples, Literals, and Schema Elements, while more connections can occur due to equivalence relationships between URIs, such as owl:sameAs, owl:equivalentProperty and owl:equivalentClass, since many publishers use such equivalence relationships, for declaring that their URIs are equivalent with URIs of other datasets. However, there are not available connectivity measurements (and indexes) involving more than two datasets, that cover the whole content (e.g., entities, schema, triples) or "slices" (e.g., triples for a specific entity) of datasets, although they can be of primary importance for several real world tasks, such as Information Enrichment, Dataset Discovery and others. Generally, it is not an easy task to find the connections among the datasets, since there exists a big number of LOD datasets and the transitive and symmetric closure of equivalence relationships should be computed for not missing connections. For this reason, we introduce scalable methods and algorithms, (a) for performing the computation of transitive and symmetric closure for equivalence relationships (since they can produce more connections between the datasets); (b) for constructing dedicated global semantics-aware indexes that cover the whole content of datasets; and (c) for measuring the connectivity among two or more datasets. Finally, we evaluate the speedup of the proposed approach, while we report comparative results for over two billion triples.

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.…”

“…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:…”

Foreword to the Special Issue: “Semantics for Big Data Integration”

“…In "A Hybrid Information Mining Approach for Knowledge Discovery in Cardiovascular Disease (CVD)", the authors combine different techniques of data exploration with the aim of extracting information and knowledge from a semantically rich dataset in the healthcare context [2]. In "High Performance Methods for Linked Open Data Connectivity Analytics" [3], the authors introduce scalable methods and algorithms to find all the connections among the Linked Open Data (LOD) Cloud datasets. In "LOD for Data Warehouses: Managing the Ecosystem Co-Evolution" [4], the authors propose and implement a management approach for the evolution of data warehouses with internal and external data published in LOD formats.…”