A mechanism is provided in a data processing system for automatic case assignment. The mechanism extracts features from a machine readable form of a case to be assigned. An expertise classifier generates an initial case assignment matrix matching the case to a plurality of caseworkers based on the extracted features, a caseworker relationship graph, and an entity relationship graph. A personnel filter filters the initial case assignment matrix based on expertise, availability, and caseload of the plurality of caseworkers to form a final caseworker assignment. The mechanism assigns the case to an identified caseworker based on the final caseworker assignment.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, in a data processing system, for automatic case assignment, the method comprising: extracting, by the data processing system, features from a machine readable form of a case to be assigned; generating, by an expertise classifier in the data processing system, an initial case assignment matrix matching the case to a plurality of caseworkers based on the extracted features, a caseworker relationship graph, and an entity relationship graph; filtering, by a personnel filter in the data processing system, the initial case assignment matrix based on expertise, availability, and caseload of the plurality of caseworkers to form a final caseworker assignment; and assigning, by the data processing system, the case to an identified caseworker based on the final caseworker assignment. 2. The method of claim 1, further comprising training the expertise classifier using prior case assignments, comprising: extracting features from the prior case assignments; building a training dataset from the features of the prior case assignments; constructing the caseworker relationship graph from the features of the prior case assignments; constructing the entity relationship graph from the features of the prior case assignments; and training the expertise classifier based on the training dataset, the caseworker relationship graph, and the entity relationship graph to form a machine learning model, wherein the expertise classifier generates the initial case assignment matrix based on the features extracted from the case using the machine teaming model. 3. The method of claim 1, wherein generating the initial case assignment matrix comprises classifying named entities in the case using a nave Bayes probabilistic classifier. 4. The method of claim 3, wherein generating the initial case assignment matrix comprises determining entity weightings for the named entities by walking the entity relationship graph. 5. The method of claim 1, wherein generating the initial case assignment matrix comprises determining a centrality value for each caseworker based on the caseworker relationship graph. 6. The method of claim 1, wherein extracting features comprises extracting keywords and named entities from the case. 7. The method of claim 1, further comprising converting text of the case from speech or scanned text. 8. The method of claim 1, wherein filtering the initial case assignment matrix comprises for each given caseworker: determining a first probability, P1 that the given caseworker has expertise to complete the case based on a centrality value of the caseworker; determining a second probability, P2, that the given caseworker will be available to complete the case based on a schedule of the caseworker; determining a third probability, P3, that the given caseworker will complete the case based on a weighted caseworker caseload. 9. The method of claim 8, wherein filtering the initial case assignment matrix further comprises determining a centrality value of the given caseworker using the caseworker relationship graph and determining the first probability based on the centrality value of the given caseworker. 10. The method of claim 8, wherein filtering the initial case assignment matrix further comprises determining a weighted caseworker caseload of the given caseworker based on a current caseload of the given caseworker and determining the third probability based on the weighted caseworker caseload. 11. The method of claim 10, wherein determining the weighted caseworker caseload comprises determining the weighted caseworker caseload based on at least one of a number of cases currently assigned to the given caseworker, sizes of cases currently assigned to the given caseworker, complexity of cases currently assigned to the given caseworker determined by user evaluation, or complexity of cases currently assigned to the given caseworker determined by machine evaluation of features. 12. The method of claim 8, wherein filtering the initial case assignment matrix further comprises determining a final probability, Pf; of assigning the given caseworker to the case as follows: Pf=P1*P2*P3 wherein the personnel filter identifies a caseworker having a highest final probability as the identified caseworker. 13. A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to: extract features from a machine readable form of a case to be assigned; generate, by an expertise classifier, an initial case assignment matrix matching the case to a plurality of caseworkers based on the extracted features, a caseworker relationship graph, and an entity relationship graph; filter, by a personnel filter, the initial case assignment matrix based on expertise, availability, and caseload of the plurality of caseworkers to form a final caseworker assignment; and assign the case to an identified caseworker based on the final caseworker assignment. 14. The computer program product of claim 13, wherein the computer readable program further causes the computing device to train the expertise classifier using prior case assignments, comprising: extracting features from the prior case assignments; building a training dataset from the features of the prior case assignments; constructing the caseworker relationship graph from the features of the prior case assignments; constructing the entity relationship graph from the features of the prior case assignments; and training the expertise classifier based on the training dataset, the caseworker relationship graph, and the entity relationship graph to form a machine learning model, wherein the expertise classifier generates the initial case assignment matrix based on the features extracted from the case using the machine learning model. 15. The computer program product of claim 13, wherein filtering the initial case assignment matrix comprises for each given caseworker: determining a first probability, P1, that the given caseworker has expertise to complete the case based on a centrality value of the caseworker; determining a second probability, P2, that the given caseworker will be available to complete the case based on a schedule of the caseworker; determining a third probability, P3, that the given caseworker will complete the case based on a weighted caseworker caseload. 16. The computer program product of claim 15, wherein filtering the initial case assignment matrix further comprises determining a centrality value of the given caseworker using the caseworker relationship graph and determining the first probability based on the centrality value of the given caseworker. 17. The computer program product of claim 15, wherein filtering the initial case assignment matrix further comprises determining a weighted caseworker caseload of the given caseworker based on a current caseload of the given caseworker and determining the third probability based on the weighted caseworker caseload. 18. The computer program product of claim 17, wherein determining the weighted caseworker caseload comprises determining the weighted caseworker caseload based on at least one of a number of cases currently assigned to the given caseworker, sizes of cases currently assigned to the given caseworker, complexity of cases currently assigned to the given caseworker determined by user evaluation, or complexity of cases currently assigned to the given caseworker determined by machine evaluation of features. 19. The computer program product of claim 15, wherein filtering the initial case assignment matrix further comprises determining a final probability, Pf; of assigning the given caseworker to the case as follows: Pf=P1*P2*P3, wherein the personnel filter identifies a caseworker having a highest final probability as the identified caseworker. 20. An apparatus comprising: a processor; and a memory coupled to the processor, wherein the memory comprises instructions which, when executed by the processor, cause the processor to: extract features from a machine readable form of a case to be assigned; generate, by an expertise classifier, an initial case assignment matrix matching the case to a plurality of caseworkers based on the extracted features, a caseworker relationship graph, and an entity relationship graph; filter, by a personnel filter, the initial case assignment matrix based on expertise, availability, and caseload of the plurality of caseworkers to form a final caseworker assignment; and assign the case to an identified caseworker based on the final caseworker assignment.