mlsanityData to 7 Oct 2026

SOSA: A Lightweight Ontology for Sensors, Observations, Samples, and Actuators

arXiv 1805.09979 ↗First posted 25 May 2018cs.AIcited by 27

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Abstract (arXiv)

The Sensor, Observation, Sample, and Actuator (SOSA) ontology provides a formal but lightweight general-purpose specification for modeling the interaction between the entities involved in the acts of observation, actuation, and sampling. SOSA is the result of rethinking the W3C-XG Semantic Sensor Network (SSN) ontology based on changes in scope and target audience, technical developments, and lessons learned over the past years. SOSA also acts as a replacement of SSN's Stimulus Sensor Observation (SSO) core. It has been developed by the first joint working group of the Open Geospatial Consortium (OGC) and the World Wide Web Consortium (W3C) on Spatial Data on the Web. In this work, we motivate the need for SOSA, provide an overview of the main classes and properties, and briefly discuss its integration with the new release of the SSN ontology as well as various other alignments to specifications such as OGC's Observations and Measurements (O&M), Dolce-Ultralite (DUL), and other prominent ontologies. We will also touch upon common modeling problems and application areas related to publishing and searching observation, sampling, and actuation data on the Web. The SOSA ontology and standard can be accessed at urlhttps://www.w3.org/TR/vocab-ssn/.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 May 2018.

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Citing papers per year: 2021: 2; 2022: 5; 2023: 4; 2024: 7; 2025: 1; 2026: 8.

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  10. 2026A Pythonic Functional Approach for Semantic Data Harmonisation in the ILIAD Projectcited by 0
  11. 2026HOME-KGQA: A Benchmark Dataset for Multimodal Knowledge Graph Question Answering on Household Daily Activitiescited by 0
  12. 2026Enabling Cloud-Level Accuracy in Edge AI through IoT Data Preprocessingcited by 0
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  14. 2026RAG-Based Auto-Configuration for Industrial Fieldbus Devicescited by 0
  15. 2026GeoOutageBench: Benchmarking Ambiguity-aware, Ontology-grounded Geospatiotemporal KGQA for Multimodal Power Outage and Resilience Analysiscited by 0