Embodiments for cause identification in audit data by a processor. A probabilistic logical representation is extracted from text data representing a knowledge domain according to an ontology to identify one or more reoccurring problems of the knowledge domain. A root cause and one or more causal factors of the one or more reoccurring problems is automatically identified using the logical representation such that the identifying associates a confidence level for the root cause and the one or more causal factors.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, by a processor, for root cause identification in audit data, comprising: extracting a logical representation of text data representing a knowledge domain according to an ontology to identify one or more reoccurring problems of the knowledge domain; and automatically identifying a root cause and one or more causal factors of the one or more reoccurring problems using the logical representation such that the identifying associates a confidence level for the root cause and the one or more causal factors. 2. The method of claim 1, further includes compressing the text data upon processing the text data using a lexical analysis, parsing, extraction of concepts, semantic analysis, or a combination thereof. 3. The method of claim 1, wherein the extracting further includes converting the text data into a first order probabilistic logical (PL) representation of the knowledge domain. 4. The method of claim 1, further including: clustering similar reoccurring problems together from the logical representation and the ontology; and clustering semantically related text to remove synonyms from the text data. 5. The method of claim 1, further including inferring the one or more causal factors with each of the one or more reoccurring problems identified in the text data and a solution provided in the ontology for each of the one or more reoccurring problems. 6. The method of claim 1, further including using natural language processing (NLP) to provide logical form evidence used for identifying the one or more causal factors between the one or more reoccurring problems and the logical representation. 7. The method of claim 1, further including using natural language processing (NLP) to provide logical form evidence for identifying the root cause between the one or more reoccurring problems and the logical representation. 8. A system for root cause identification in audit data, comprising: one or more computers with executable instructions that when executed cause the system to: extract a logical representation of text data representing a knowledge domain according to an ontology to identify one or more reoccurring problems of the knowledge domain; and automatically identify a root cause and one or more causal factors of the one or more reoccurring problems using the logical representation such that the identifying associates a confidence level for the root cause and the one or more causal factors. 9. The system of claim 8, wherein the executable instructions compress the text data upon processing the text data using a lexical analysis, parsing, extraction of concepts, semantic analysis, or a combination thereof. 10. The system of claim 8, wherein the executable instructions convert the text data into a first order probabilistic logical (PL) representation of the knowledge domain. 11. The system of claim 8, wherein the executable instructions: cluster similar reoccurring problems together from the logical representation and the ontology; and cluster semantically related text to remove synonyms from the text data. 12. The system of claim 8, wherein the executable instructions infer the one or more causal factors with each of the one or more reoccurring problems identified in the text data and a solution provided in the ontology. 13. The system of claim 8, wherein the executable instructions use natural language processing (NLP) to provide logical form evidence used for identifying the one or more causal factors between the one or more reoccurring problems and the logical representation. 14. The system of claim 8, wherein the executable instructions use natural language processing (NLP) to provide logical form evidence for identifying the root cause between the one or more reoccurring problems and the logical representation. 15. A computer program product for, by a processor, root cause identification in audit data, 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 extracts a logical representation of text data representing a knowledge domain according to an ontology to identify one or more reoccurring problems of the knowledge domain; and an executable portion that automatically identifies a root cause and one or more causal factors of the one or more reoccurring problems using the logical representation such that the identifying associates a confidence level for the root cause and the one or more causal factors. 16. The computer program product of claim 15, further including an executable portion that compresses the text data upon processing the text data using a lexical analysis, parsing, extraction of concepts, semantic analysis, or a combination thereof. 17. The computer program product of claim 15, further including an executable portion that converts the text data into a first order probabilistic logical (PL) representation of the knowledge domain. 18. The computer program product of claim 15, further including an executable portion that: clusters similar reoccurring problems together from the logical representation and the ontology; and clusters semantically related text to remove synonyms from the text data. 19. The computer program product of claim 15, further including an executable portion that infers the one or more causal factors with each of the one or more reoccurring problems identified in the text data and a solution provided in the ontology. 20. The computer program product of claim 15, further including an executable portion that: uses natural language processing (NLP) to provide logical form evidence used for identifying the one or more causal factors between the one or more reoccurring problems and the logical representation; and uses natural language processing (NLP) to provide logical form evidence for identifying the root cause between the one or more reoccurring problems and the logical representation.