Methods, systems, and computer program products for expressive temporal predictions over semantically-driven time windows are provided herein. A computer-implemented method includes identifying, within a knowledge graph pertaining to a given prediction, a subset of the knowledge graph related to one or more predicted training examples, wherein the subset comprises (i) a set of nodes and (ii) one or more relationships among the set of nodes; determining, for the identified subset, one or more snapshots of the knowledge graph relevant to the given prediction; quantifying a validity window for the one or more predicted training examples, wherein the validity window comprises a temporal bound for prediction validity; and computing a validity window for the given prediction based on the quantified validity window for the one or more predicted training examples.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method, comprising: identifying, within a knowledge graph pertaining to a given prediction, a subset of the knowledge graph related to one or more predicted training examples, wherein the subset comprises (i) a set of nodes and (ii) one or more relationships among the set of nodes; determining, for the identified subset, one or more snapshots of the knowledge graph relevant to the given prediction; quantifying a validity window for the one or more predicted training examples, wherein the validity window comprises a temporal bound for prediction validity; and computing a validity window for the given prediction based on the quantified validity window for the one or more predicted training examples. 2. The computer-implemented method of claim 1, wherein said one or more snapshots of the knowledge graph relevant to the given prediction comprise (i) one or more historic snapshots and (ii) one or more predicted future snapshots. 3. The computer-implemented method of claim 2, wherein said determining the one or more historic snapshots comprises employing a version of the knowledge graph that is enhanced with historic information for each entity in the knowledge graph and each relation among the entities in the knowledge graph. 4. The computer-implemented method of claim 2, wherein the one or more historic snapshots comprise multiple historic snapshots differentiated based on a learned threshold. 5. The computer-implemented method of claim 2, wherein the one or more predicted future snapshots comprise multiple predicted future snapshots differentiated based on a learned threshold. 6. The computer-implemented method of claim 2, comprising: predicting the one or more future snapshots using at least one link discovery technique given the current state of the knowledge graph. 7. The computer-implemented method of claim 6, wherein the at least one link discovery technique comprises matrix completion. 8. The computer-implemented method of claim 1, wherein the given prediction comprises a predicted value of a particular asset embodied within the knowledge graph. 9. The computer-implemented method of claim 1, comprising: extracting one or more features from the one or more snapshots of the knowledge graph. 10. The computer-implemented method of claim 9, comprising: training a prediction model based on the one or more extracted features. 11. The computer-implemented method of claim 10, comprising: updating the given prediction based on utilization of the prediction model. 12. The computer-implemented method of claim 1, wherein software is provided as a service in a cloud environment. 13. The computer-implemented method of claim 1, wherein said quantifying the validity window for the one or more predicted training examples comprises employing a time series analysis to the one or more snapshots of the knowledge graph. 14. A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a device to cause the device to: identify, within a knowledge graph pertaining to a given prediction, a subset of the knowledge graph related to one or more predicted training examples, wherein the subset comprises (i) a set of nodes and (ii) one or more relationships among the set of nodes; determine, for the identified subset, one or more snapshots of the knowledge graph relevant to the given prediction; quantify a validity window for the one or more predicted training examples, wherein the validity window comprises a temporal bound for prediction validity; and compute a validity window for the given prediction based on the quantified validity window for the one or more predicted training examples. 15. The computer program product of claim 14, wherein the given prediction comprises a predicted value of a particular asset embodied within the knowledge graph. 16. The computer program product of claim 14, wherein the program instructions executable by a computing device further cause the computing device to: extract one or more features from the one or more snapshots of the knowledge graph. 17. The computer program product of claim 16, wherein the program instructions executable by a computing device further cause the computing device to: train a prediction model based on the one or more extracted features. 18. A system comprising: a memory; and at least one processor coupled to the memory and configured for: identifying, within a knowledge graph pertaining to a given prediction, a subset of the knowledge graph related to one or more predicted training examples, wherein the subset comprises (i) a set of nodes and (ii) one or more relationships among the set of nodes; determining, for the identified subset, one or more snapshots of the knowledge graph relevant to the given prediction; quantifying a validity window for the one or more predicted training examples, wherein the validity window comprises a temporal bound for prediction validity; and computing a validity window for the given prediction based on the quantified validity window for the one or more predicted training examples. 19. The system of claim 18, wherein the at least one processor is further configured for: extracting one or more features from the one or more snapshots of the knowledge graph. 20. The system of claim 19, wherein the at least one processor is further configured for: training a prediction model based on the one or more extracted features.