High Performance Methods for Linked Open Data Connectivity Analytics

• Published in: Information (Basel) 2018-06, Vol.9 (6), p.134
• Main Authors: Mountantonakis, Michalis, Tzitzikas, Yannis
• Format: Article
• Language: English
• Subjects: Algorithms; big data; Connectivity; content-based connectivity measurements; dataset discovery; Datasets; Equivalence; information enrichment; lattice of measurements; Linked Data; LOD scale analytics; MapReduce; Open data; Performance indices; Resource Description Framework-RDF; semantic web; Semantics
• ISSN: 2078-2489; 2078-2489
• DOI: 10.3390/info9060134

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.