SOSA-SHACL: Shapes Constraint for the Sensor, Observation, Sample, and Actuator Ontology

Rui Zhu, Cogan Shimizu, Shirly Stephen, Lu Zhou, Ling Cai, Gengchen Mai, Krzysztof Janowicz, Mark Schildhauer, Pascal Hitzler

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

6 Citations (Scopus)

Abstract

The explosive growth of the Linked Data on the Web has greatly facilitated collecting data from remote sensors, from air quality sensors spread out across a city, to seismograph stations spread across the entire world. Integrating these heterogeneous data can be quite challenging; however one can achieve this through the use of available W3C standards to create a knowledge graph. For this use case, the W3C also provides a standard, the Sensor, Observation, Sample, Actuator (SOSA) Ontology, that allows for the semantic encoding of sensors and their observations. However, even with the guidance of this standard, it may be difficult to produce a correct graph with high fidelity from heterogeneous sources. In this paper we present a set of (data) shape constraints, called SOSA-SHACL, for the SOSA ontology using a data validation language, namely the W3C standard SHACL (Shape Constraint Language). These constraints enable us to evaluate whether the modeled observations in our Knowledge Graph comply with the SOSA recommendations. Furthermore, we show through several case studies how the closed world assumption plays a role in the process of designing such shape constraints, especially as SOSA is based on the open world assumption.

Original languageEnglish
Title of host publicationProceedings of the 10th International Joint Conference on Knowledge Graphs, IJCKG 2021
PublisherAssociation for Computing Machinery (ACM)
Pages99-107
Number of pages9
ISBN (Electronic)9781450395656
DOIs
Publication statusPublished - 6 Dec 2021
Event10th International Joint Conference on Knowledge Graphs, IJCKG 2021 - Virtual, Online, Thailand
Duration: 6 Dec 20218 Dec 2021

Publication series

NameACM International Conference Proceeding Series

Conference

Conference10th International Joint Conference on Knowledge Graphs, IJCKG 2021
Country/TerritoryThailand
CityVirtual, Online
Period6/12/218/12/21

Bibliographical note

Funding Information:
The authors acknowledge support by the National Science Foundation under Grant 2033521 A1: KnowWhereGraph: Enriching and Linking Cross-Domain Knowledge Graphs using Spatially-Explicit AI Technologies. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Publisher Copyright:
© 2021 Owner/Author.

Keywords

  • knowledge graph quality assessment and refinement
  • RDF validation
  • sensors and observations

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