Modelling and analysing dynamic linked data using RDF and SPARQL

Tobias Käfer, Alexandra Wins, Maribel Acosta

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

Analyses of dynamic Linked Data are inherently dependent on changes in RDF graphs (logical level) and what happens on the HTTP and networking level (physical level). However, these dependencies have been reflected in previous works only to a limited extent, which may lead to inaccurate conclusions about the dynamics of the data. To overcome this limitation, we tackle the problem of modelling dynamic Linked Data to capture changes both at the logical and physical level of Linked Data. We present our work in progress in this paper. We propose an RDF model of descriptions of both the HTTP requests/responses/networking errors made when downloading, and the RDF data thus obtained. The model allows for carrying out more comprehensive analyses of dynamic Linked Data in a declarative fashion. We present a processing pipeline to distil such modelled RDF data from datasets created using LDspider, such as the Dynamic Linked Data Observatory. We show the usefulness of our model by repeating three analyses of a previous paper on the Dynamic Linked Data Observatory using SPARQL queries, which we executed on data that follows our proposed model on three SPARQL engines.

Original languageEnglish
JournalCEUR Workshop Proceedings
Volume1927
StatePublished - 2017
Externally publishedYes
Event4th International Workshop on Dataset PROFIling and fEderated Search for Web Data, PROFILES 2017 - Vienna, Austria
Duration: 22 Oct 2017 → …

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