A computer-implemented method of analyzing text documents, includes identifying a relationship in a text document associated with an entity, building a predictive model from training data, in response to said identifying a relationship, wherein the predictive model includes a prediction error, and determining whether to store the identified relationship in memory, based on the prediction error.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method of analyzing text documents, comprising: identifying a relationship in a text document associated with an entity; building a predictive model from training data, in response to said identifying a relationship, wherein the predictive model includes a prediction error; and determining whether to store the identified relationship in memory, based on the prediction error. 2. The method of claim 1, wherein the determining of whether to store the identified relationship comprises augmenting a knowledge graph with the identified relationship if a the prediction error was reduced when a feature incorporating the relationship was used in the predictive model. 3. The method of claim 1, wherein the relationship comprises a triple which includes a set of concepts related to a second set of concepts, and the determining of whether to store the identified relationship is based on a conciseness of the triple. 4. The method of claim 1, wherein the relationship comprises a triple which includes a set of concepts related to a second set of concepts, and the determining of whether to store the identified relationship is based on a support for the triple in the text document. 5. The method of claim 1, wherein the determining of whether to store the identified relationship comprises determining to store the identified relationship if a value of the prediction error is below a threshold value. 6. The method of claim 1, wherein the relationship comprises a triple which relates a generalization of a target entity through a relationship type. 7. The method of claim 1, wherein the relationship comprises a plurality of relationships that comprise taxonomies of concept and relation types. 8. The method of claim 7, further comprising: generating a plurality of features for the entity from the plurality of relationships. 9. The method of claim 8, further comprising: selecting a level of abstraction of the plurality of relationships. 10. The method of claim 1, wherein at least one of the identifying of the relationship, the building of the predictive model, and the storing of the identified relationship are performed in a cloud-computing environment. 11. A system for analyzing text documents, comprising: a processor; and a memory, the memory operably coupled to the processor and storing instructions to cause the processor to: identify a relationship in a text document associated with an entity; build a predictive model from training data, in response to said identifying a relationship, wherein the predictive model includes a prediction error; and determine whether to store the identified relationship in memory, based on the prediction error. 12. The system of claim 11, wherein the processor adds the relationship to the database by augmenting a knowledge graph with the relationship if the prediction error over the training data was reduced when the feature was used in the predictive model. 13. The system of claim 11, wherein the processor decides whether to add the relationship to the database based on a conciseness of a triple which includes a set of concepts related to a second set of concepts. 14. The system of claim 11, wherein the processor decides whether to add the relationship to the database based on a support for a triple which includes the number of occurrences of the triple in the extracted concepts and relationships from the text documents. 15. The system of claim 11, wherein the processor adds the relationship to the database if a value of the prediction error using the feature is below a threshold value. 16. The system of claim 11, wherein the relationship comprises a triple which relates a generalization of a target entity through a relationship type. 17. The system of claim 11, wherein the relationship comprises a plurality of relationships that comprise taxonomies of concept and relationship types. 18. The system of claim 17, wherein the processor generates a plurality of features for the entities from the plurality of relationships, and wherein the processor selects a level of abstraction of the relationships. 19. The system of claim 11, wherein the processor performs at least one of the identifying of the relationship, the building of the predictive model, and the storing of the identified relationship in a cloud-computing environment. 20. A computer program product for analyzing text documents, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to the computer to: identify a relationship in a text document associated with an entity; build a predictive model from training data, in response to said identifying a relationship, wherein the predictive model includes a prediction error; and determine whether to store the identified relationship in memory, based on the prediction error.