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Computing Linked Data On-Demand Using the VOLT Proxy

  • Blake RegaliaEmail author
  • Krzysztof Janowicz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9989)

Abstract

The Linked Data paradigm has changed how data on the Web is published, retrieved, and interlinked, thereby enabling modern question answering systems and contributing to the spread of open data. With the increasing size, interlinkage, and complexity of the Linked Data cloud, the focus is now shifting towards strategies and technologies to ensure that Linked Data can also succeed as an infrastructure. This raises questions about the sustainability of query endpoints, the reproducibility of scientific experiments conducted using Linked Data, the lack of established quality metrics, as well as the need for improved ontology alignment and query federation techniques. One core issue that needs to be addressed is the trade-off between storing data and computing them on-demand. Data that is derived from already stored data, changes frequently in space and time, or is the output of some workflow, should be computed. However, such functionality is not readily available on the Linked Data cloud today. To address this issue, we have developed a transparent SPARQL proxy that enables the on-demand computation of Linked Data together with the provenance information required to understand how the data were derived. Here, we demonstrate how the proxy works under the hood by applying it to the computation of cardinal directions between geographic features in DBpedia.

Keywords

Link Data Cardinal Direction SPARQL Query Computable Property Question Answering System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Regalia, B., Janowicz, K., Gao, S.: VOLT: a provenance-producing, transparent SPARQL proxy for the on-demand computation of linked data and its application to spatiotemporally dependent data. In: Sack, H., Blomqvist, E., d’Aquin, M., Ghidini, C., Ponzetto, S.P., Lange, C. (eds.) ESWC 2016. LNCS, vol. 9678, pp. 523–538. Springer, Heidelberg (2016). doi: 10.1007/978-3-319-34129-3_32 CrossRefGoogle Scholar
  2. 2.
    Rietveld, L., Verborgh, R., Beek, W., Vander Sande, M., Schlobach, S.: Linked data-as-a-service: the semantic web redeployed. In: Gandon, F., Sabou, M., Sack, H., d’Amato, C., Cudré-Mauroux, P., Zimmermann, A. (eds.) ESWC 2015. LNCS, vol. 9088, pp. 471–487. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-18818-8_29 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.STKO LabUniversity of CaliforniaSanta BarbaraUSA

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