A policy violation detection computer-implemented method, system, and computer program product, includes extracting a policy activity from a policy, the policy activity including an actor in the policy, an object of the policy, an action of the policy, and policy scope metadata, capturing a transaction by a user including metadata of the transaction, translating the transaction by the user into an actor in the transaction, an action of the transaction, and an object of the transaction, and alerting the user of a policy violation by navigating a knowledge graph is-a hierarchy to relate the actor in the transaction to the actor in the policy, the object of the transaction to an object of the policy, and the action of the transaction to an action of the policy activity.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented policy violation detection method, the method comprising: extracting a policy activity from a policy, the policy activity including an actor in the policy, an object of the policy, an action of the policy, and policy scope metadata; capturing a transaction by a user including metadata of the transaction; translating the transaction by the user into an actor in the transaction, an action of the transaction, and an object of the transaction; and alerting the user of a policy violation by navigating a knowledge graph is-a hierarchy to relate the actor in the transaction to the actor in the policy, the object of the transaction to an object of the policy, and the action of the transaction to an action of the policy activity. 2. The computer-implemented method of claim 1, wherein the alerting alerts the user of the at least one policy violation by running a rule-based inference model to predict if a portion of the policy activity is violated by a future action with the object of the transaction by the user based on an inference rule between the natural language of the transaction and the policy activity. 3. The computer-implemented method of claim 2, wherein the rule-based inference model is selected from a group consisting of: an inference rule to check the object against the policy if an object is acquired; an inference rule to check if the action enables a second action that can violate the policy in the future if the user does the action without the object; and an inference rule based on if the user changes the object or changes a state of the object that can violate the policy in the future. 4. The computer-implemented method of claim 1, further comprising filtering and ranking relevant sub-policies in the policy by a criticality level of the policy violation when the alerting alerts the user of a plurality of policy violations. 5. The computer-implemented method of claim 1, wherein the alerting the user of at least one a policy violation alerts at least one of the user, a friend of the user, a party to the transaction, a party to the policy, an owner of the transaction, and an owner of the policy. 6. The computer-implemented method of claim 1, wherein a type of the transaction captured by the capturing is selected from a group consisting of: a sale/purchase transaction; an invest/divest transaction; a borrowing transaction; a construction transaction; an invitation accept/decline transaction; an entering/leaving a location transaction; an entering/leaving a location during a time period transaction; and a transaction on a computer system or device. 7. The computer-implemented method of claim 1, wherein the alerting communicates the activity causing the policy violation to the user if the knowledge graph indicates that a specific type of the object is of a given type, wherein the given type comprises a known classification of a string of tokens, wherein the given type is in a graph, wherein the graph is selected from a group consisting of: Wordnet; Freebase; and Yago. 8. The computer-implemented method of claim 1, wherein an inference rule is applied to the transaction resulting in a potential future action which is then checked for the policy violation by navigating the knowledge graph is-a hierarchy. 9. The computer-implemented method of claim 1, wherein the action, the object, and the actor in the transaction are stated in natural language where the action comprises a verb or a verb phrase of a sentence, the object comprises a noun or a noun phrase object of the same sentence, and the actor comprises a noun or a noun phrase subject of that sentence. 10. The computer-implemented method of claim 1, embodied in a cloud-computing environment. 11. A computer program product for policy violation detection, the 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 perform: extracting a policy activity from a policy, the policy activity including an actor in the policy, an object of the policy, an action of the policy, and policy scope metadata; capturing a transaction by a user including metadata of the transaction; translating the transaction by the user into an actor in the transaction, an action of the transaction, and an object of the transaction; and alerting the user of a policy violation by navigating a knowledge graph is-a hierarchy to relate the actor in the transaction to the actor in the policy, the object of the transaction to an object of the policy, and the action of the transaction to an action of the policy activity. 12. The computer program product of claim 11, wherein the alerting alerts the user of the at least one policy violation by running a rule-based inference model to predict if a portion of the policy activity is violated by the future action with the object of the transaction by the user based on an inference rule between the natural language of the transaction and the policy activity. 13. The computer program product of claim 12, wherein the rule-based inference model comprises inferences of: an inference rule to check the object against the policy if an object is acquired; an inference rule to check if the action enables a second action that can violate the policy in the future if the user does the action without the object; and an inference rule based on if the user changes the object or changes a state of the object that can violate the policy in the future. 14. The computer program product of claim 11, further comprising filtering and ranking relevant sub-policies in the policy by a criticality level of the policy violation when the alerting alerts the user of a plurality of policy violations. 15. The computer program product of claim 11, wherein the alerting the user of at least one a policy violation alerts at least one of the user, a friend of the user, a party to the transaction, a party to the policy, an owner of the transaction, and an owner of the policy. 16. A policy violation detection system, said system comprising: a processor; and a memory, the memory storing instructions to cause the processor to perform: extracting policy activity from a policy, the policy activity including an actor in the policy, an object of the policy, and policy scope metadata; capturing a transaction by a user including metadata of the transaction; translating the transaction by the user into natural language indicating an actor in the transaction, a verb phrase for an object of the transaction, and a noun phrase of the transaction; and alerting the user of a policy violation by navigating a knowledge graph is-hierarchy to relate the natural language of a future action with the object of the transaction to the portion of the policy activity, relate the object of the transaction to the object of the policy, and verify that the object of the policy is related to the future action with the object of the transaction 17. The system of claim 16, wherein the alerting alerts the user of the at least one policy violation by running a rule-based inference model to predict if a portion of the policy activity is violated by the future action with the object of the transaction by the user based on an inference rule between the natural language of the transaction and the policy activity. 18. The system of claim 17, wherein the rule-based inference model is selected from a group consisting of: an inference rule to check the object against the policy if an object is acquired; an inference rule to check if the action enables a second action that can violate the policy in the future if the user does the action without the object; and an inference rule based on if the user changes the object or changes a state of the object that can violate the policy in the future. 19. The system of claim 16, further comprising filtering and ranking relevant sub-policies in the policy by a criticality level of the policy violation when the alerting alerts the user of a plurality of policy violations. 20. The system of claim 16, embodied in a cloud-computing environment.