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 language | English |
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Title of host publication | Proceedings of the 10th International Joint Conference on Knowledge Graphs, IJCKG 2021 |
Publisher | Association for Computing Machinery (ACM) |
Pages | 99-107 |
Number of pages | 9 |
ISBN (Electronic) | 9781450395656 |
DOIs | |
Publication status | Published - 6 Dec 2021 |
Event | 10th International Joint Conference on Knowledge Graphs, IJCKG 2021 - Virtual, Online, Thailand Duration: 6 Dec 2021 → 8 Dec 2021 |
Publication series
Name | ACM International Conference Proceeding Series |
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Conference
Conference | 10th International Joint Conference on Knowledge Graphs, IJCKG 2021 |
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Country/Territory | Thailand |
City | Virtual, Online |
Period | 6/12/21 → 8/12/21 |
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