Embodiments for using virtual sensor models in an internet of things (IoT) environment by a processor. One or more virtual sensor models are automatically identified according to a semantic graph, having a knowledge domain that links and describes a relationship between observed variables associated with one or more sensors with unobserved variables associated with the IoT environment. The one or more virtual sensor models may be selected for deployment in the IoT environment according to one or more combinations of virtual sensor inputs.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, by a processor, for using virtual sensor models in an internet of things (IoT) environment, comprising: automatically identifying one or more virtual sensor models according to a semantic graph, having a knowledge domain that links and describes a relationship between observed variables associated with one or more sensors with unobserved variables associated with the IoT environment; and selecting the one or more virtual sensor models for deployment in the IoT environment according to one or more combinations of virtual sensor inputs. 2. The method of claim 1, further including creating the semantic graph from a selection of data points from the one or more sensors or a database. 3. The method of claim 1, further including identifying those of the one or more virtual sensor models having a graph pattern that match at least one of the one or more combinations of virtual sensor inputs. 4. The method of claim 1, further including apply graph patterns of the one or more combinations of virtual sensor inputs to the one or more virtual sensor models to identify locations for the deployment. 5. The method of claim 1, further including training the one or more virtual sensor models according to historical data or simulated sensor data using machine learning. 6. The method of claim 5, further including selecting the one or more virtual sensor models stored in a database utilizing the semantic graph or applying a predictive model to the one or more combinations of virtual sensor inputs. 7. The method of claim 1, further including estimating virtual sensor model output values and confidence levels for each one of the one or more virtual sensor models according to applicability and variance of the one or more combinations of virtual sensor inputs. 8. A system for using virtual sensor models in an internet of things (IoT) environment, comprising: a processor, operational within and between a distributed computing environment, that automatically identifies one or more virtual sensor models according to a semantic graph, having a knowledge domain that links and describes a relationship between observed variables associated with one or more sensors with unobserved variables associated with the IoT environment; and selects the one or more virtual sensor models for deployment in the IoT environment according to one or more combinations of virtual sensor inputs. 9. The system of claim 8, wherein the processor creates the semantic graph from a selection of data points from the one or more sensors or a database. 10. The system of claim 8, wherein the processor identifies those of the one or more virtual sensor models having a graph pattern that match at least one of the one or more combinations of virtual sensor inputs. 11. The system of claim 8, wherein the processor applies graph patterns of the one or more combinations of virtual sensor inputs to the one or more virtual sensor models to identify locations for the deployment. 12. The system of claim 8, wherein the processor trains the one or more virtual sensor models according to historical data or simulated sensor data using machine learning. 13. The system of claim 8, wherein the processor selects the one or more virtual sensor models stored in a database utilizing the semantic graph or applying a predictive model to the one or more combinations of virtual sensor inputs. 14. The system of claim 8, wherein the processor estimates virtual sensor model output values and confidence levels for each one of the one or more virtual sensor models according to applicability and variance of the one or more combinations of virtual sensor inputs. 15. A computer program product for, by a processor, using virtual sensor models in an internet of things (IoT) environment, the computer program product comprising a non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising: an executable portion that automatically identifies one or more virtual sensor models according to a semantic graph, having a knowledge domain that links and describes a relationship between observed variables associated with one or more sensors with unobserved variables associated with the IoT environment; and an executable portion that selects the one or more virtual sensor models for deployment in the IoT environment according to one or more combinations of virtual sensor inputs. 16. The computer program product of claim 15, further including an executable portion that creates the semantic graph from a selection of data points from the one or more sensors or a database. 17. The computer program product of claim 15, further including an executable portion that: identifies those of the one or more virtual sensor models having a graph pattern that match at least one of the one or more combinations of virtual sensor inputs; and applies graph patterns of the one or more combinations of virtual sensor inputs to the one or more virtual sensor models to identify locations for the deployment. 18. The computer program product of claim 15, further including an executable portion that trains the one or more virtual sensor models according to historical data or simulated sensor data using machine learning. 19. The computer program product of claim 15, further including an executable portion that selects the one or more virtual sensor models stored in a database utilizing the semantic graph or applying a predictive model to the one or more combinations of virtual sensor inputs. 20. The computer program product of claim 15, further including an executable portion that estimates virtual sensor model output values and confidence levels for each one of the one or more virtual sensor models according to applicability and variance of the one or more combinations of virtual sensor inputs.