Difference between revisions of "KHealthAsthmaOntology"
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The Knoesis Asthma Ontology (KAO) is being designed to deduce abstractions from the asthma dataset provided by the kHealth project. | The Knoesis Asthma Ontology (KAO) is being designed to deduce abstractions from the asthma dataset provided by the kHealth project. | ||
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The asthma dataset collects data generated by IoT devices such as peak flow meter, Foobot, Fitbit, AirNow, and other Web Services obtaining parameters such as air quality, pollen index, outside humidity and temperature. | The asthma dataset collects data generated by IoT devices such as peak flow meter, Foobot, Fitbit, AirNow, and other Web Services obtaining parameters such as air quality, pollen index, outside humidity and temperature. | ||
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* the [https://bioportal.bioontology.org/ontologies/AO Asthma Ontology (AO ontology)] from BioPortal to reuse relevant concepts | * the [https://bioportal.bioontology.org/ontologies/AO Asthma Ontology (AO ontology)] from BioPortal to reuse relevant concepts | ||
* [http://xmlns.com/foaf/spec/ FOAF ontology] to describe people | * [http://xmlns.com/foaf/spec/ FOAF ontology] to describe people | ||
| − | * Weather ontology [Staroch | + | * Weather ontology [Staroch 2013] [Kofler et al. 2011]to deduce meaningful information from weather datasets. |
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Sheth, A., Jaimini, U., Thirunarayan, K., Banerjee, T.: Augmented personalized health: How smart data with iots and ai is about to change healthcare. In: IEEE RTSI. (2017) | Sheth, A., Jaimini, U., Thirunarayan, K., Banerjee, T.: Augmented personalized health: How smart data with iots and ai is about to change healthcare. In: IEEE RTSI. (2017) | ||
| − | Sheth, A., Anantharam, P., | + | Jaimini, U., Banerjee, T., Romine, W., Thirunarayan, K., Sheth, A., Kalra, M.: Investigation of an indoor air quality sensor for asthma management in children. IEEE sensors letters (2017) |
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| + | Anantharam, P., Banerjee, T., Sheth, A., Thirunarayan, K., Marupudi, S., Sridharan, V., Forbis, S.G.: Knowledge-driven personalized contextual mhealth service for asthma management in children. In: IEEE ICMS. (2015) | ||
Sheth, A., Anantharam, P., Thirunarayan, K.: khealth: Proactive personalized actionable information for better healthcare. In: PDA@ IOT at VLDB. (2014) | Sheth, A., Anantharam, P., Thirunarayan, K.: khealth: Proactive personalized actionable information for better healthcare. In: PDA@ IOT at VLDB. (2014) | ||
| − | + | Sheth, A., Anantharam, P., Henson, C.: Physical-cyber-social computing: An early 21st century approach. IEEE Intelligent Systems (2013) | |
Staroch, P.: A weather ontology for predictive control in smart homes. Master’s thesis (2013) | Staroch, P.: A weather ontology for predictive control in smart homes. Master’s thesis (2013) | ||
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Kofler, M., Reinisch, C., Kastner, W.: An intelligent knowledge representation of smart home energy parameters. In: Proceedings of the World Renewable Energy Congress (WREC). (2011) | Kofler, M., Reinisch, C., Kastner, W.: An intelligent knowledge representation of smart home energy parameters. In: Proceedings of the World Renewable Energy Congress (WREC). (2011) | ||
Revision as of 15:57, 10 July 2018
Knoesis Asthma Ontology (KAO)
The Knoesis Asthma Ontology (KAO) is being designed to deduce abstractions from the asthma dataset provided by the kHealth project.
The asthma dataset collects data generated by IoT devices such as peak flow meter, Foobot, Fitbit, AirNow, and other Web Services obtaining parameters such as air quality, pollen index, outside humidity and temperature.
The knoesis Asthma Ontology (kAO) ontology is being created and reused ontologies as follows:
- W3C Semantic Sensor Network (SSN) and Sensor, Observation, Sample, and Actuator (SOSA) ontologies to semantically annotate sensor observations (e.g., peak flow meter is a subclass of the sosa:Sensor class)
- the Asthma Ontology (AO ontology) from BioPortal to reuse relevant concepts
- FOAF ontology to describe people
- Weather ontology [Staroch 2013] [Kofler et al. 2011]to deduce meaningful information from weather datasets.
Contents
Kao Ontology: Best Practices with Perfecto (ONGOING) (ONGOING)
We follow as much as possible the PerfectO methodology to improve the quality of the ontology and its integration with useful ontology tools to provide automatic documentation visualization, etc.
kao ontology implementation (ONGOING)
References
Sheth, A., Jaimini, U., Thirunarayan, K., Banerjee, T.: Augmented personalized health: How smart data with iots and ai is about to change healthcare. In: IEEE RTSI. (2017)
Jaimini, U., Banerjee, T., Romine, W., Thirunarayan, K., Sheth, A., Kalra, M.: Investigation of an indoor air quality sensor for asthma management in children. IEEE sensors letters (2017)
Anantharam, P., Banerjee, T., Sheth, A., Thirunarayan, K., Marupudi, S., Sridharan, V., Forbis, S.G.: Knowledge-driven personalized contextual mhealth service for asthma management in children. In: IEEE ICMS. (2015)
Sheth, A., Anantharam, P., Thirunarayan, K.: khealth: Proactive personalized actionable information for better healthcare. In: PDA@ IOT at VLDB. (2014)
Sheth, A., Anantharam, P., Henson, C.: Physical-cyber-social computing: An early 21st century approach. IEEE Intelligent Systems (2013)
Staroch, P.: A weather ontology for predictive control in smart homes. Master’s thesis (2013)
Kofler, M., Reinisch, C., Kastner, W.: An intelligent knowledge representation of smart home energy parameters. In: Proceedings of the World Renewable Energy Congress (WREC). (2011)
Acknowledgments
This work is partially funded by KHealth NIH 1 R01 HD087132-01. The opinions expressed are those of the authors and do not reflect those of the sponsors.
