Knowledge automation techniques may include discovering data files from one or more content repositories, and identifying key terms in the data files. For each of the identified key terms, a frequency of occurrence of the key term in the corresponding data file, and locations of the key term in the corresponding data file can be determined. A plurality of knowledge units can be generated from the data files based on the determined frequencies of occurrence and the determined locations of the key terms.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: discovering, by a data processing system, data files from one or more content repositories; identifying, by the data processing system, key terms in the data files; for each of the identified key terms: determining, by the data processing system, a frequency of occurrence of the key term in the corresponding data file; and determining, by the data processing system, locations of the key term in the corresponding data file; generating, by the data processing system, a plurality of knowledge units from the data files based on the determined frequencies of occurrence and the determined locations of the key terms; and storing, by the data processing system, the plurality of knowledge units in a data store. 2. The method of claim 1, further comprising: converting the data files into a common data format. 3. The method of claim 1, further comprising: associating each of the knowledge units with a term vector that includes one or more key terms in the corresponding knowledge unit. 4. The method of claim 3, further comprising: selecting one of the knowledge units; performing a similarity mapping between the selected knowledge unit and the other knowledge units based on a knowledge unit distance metric computed between the term vector of the selected knowledge unit and the term vector of each of the other knowledge units; identifying one or more of the other knowledge units that has the knowledge unit distance metric being below a predetermined threshold distance; and combining the selected knowledge unit and the identified one or more of the other knowledge units into a knowledge pack. 5. The method of claim 1, wherein generating the plurality of knowledge units includes segmenting a data file into knowledge segments, and forming one or more of the knowledge units from the knowledge segments. 6. The method of claim 5, wherein the data file is segmented based on organization of content within the data file. 7. The method of claim 5, wherein the data file includes unstructured content, and the data file is segmented based on the determined locations of key terms in the data file. 8. A non-transitory computer-readable storage memory storing a plurality of instructions executable by one or more processors, the plurality of instructions comprising: instructions that cause the one or more processors to discover data files from one or more content repositories; instructions that cause the one or more processors to identify key terms in the data files; instructions that cause the one or more processors to, for each of the identified key terms, determine a frequency of occurrence of the key term in the corresponding data file, and determine locations of the key term in the corresponding data file; instructions that cause the one or more processors to generate a plurality of knowledge units from the data files based on the determined frequencies of occurrence and the determined locations of the key terms; and instructions that cause the one or more processors to store the plurality of knowledge units in a data store. 9. The non-transitory computer-readable storage memory of claim 8, wherein the plurality of instructions further comprises: instructions that cause the one or more processors to convert the data files into a common data format. 10. The non-transitory computer-readable storage memory of claim 8, wherein the plurality of instructions further comprises: instructions that cause the one or more processors to associate each of the knowledge units with a term vector that includes one or more key terms in the corresponding knowledge unit. 11. The non-transitory computer-readable storage memory of claim 10, wherein the plurality of instructions further comprises: instructions that cause the one or more processors to select one of the knowledge units; instructions that cause the one or more processors to perform a similarity mapping between the selected knowledge unit and the other knowledge units based on a knowledge unit distance metric computed between the term vector of the selected knowledge unit and the term vector of each of the other knowledge units; instructions that cause the one or more processors to identify one or more of the other knowledge units that has the knowledge unit distance metric being below a predetermined threshold distance; and instructions that cause the one or more processors to combine the selected knowledge unit and the identified one or more of the other knowledge units into a knowledge pack. 12. The non-transitory computer-readable storage memory of claim 8, wherein the plurality of instructions further comprises: instructions that cause the one or more processors to segment a data file into knowledge segments; and instructions that cause the one or more processors to form one or more of the knowledge units from the knowledge segments. 13. The non-transitory computer-readable storage memory of claim 12, wherein the data file is segmented based on organization of content within the data file. 14. The non-transitory computer-readable storage memory of claim 12, wherein the data file includes unstructured content, and the data file is segmented based on the determined locations of key terms in the data file. 15. A system comprising: one or more processors; and a memory coupled with and readable by the one or more processors, the memory configured to store a set of instructions which, when executed by the one or more processors, causes the one or more processors to: discover data files from one or more content repositories; identify key terms in the data files; for each of the identified key terms: determine a frequency of occurrence of the key term in the corresponding data file; and determine locations of the key term in the corresponding data file; generate a plurality of knowledge units from the data files based on the determined frequencies of occurrence and the determined locations of the key terms; and store the plurality of knowledge units in a data store. 16. The system of claim 15, wherein the set of instructions further comprises instructions, which when executed by the one or more processors, causes the one or more processors to: convert the data files into a common data format. 17. The system of claim 15, wherein the set of instructions further comprises instructions, which when executed by the one or more processors, causes the one or more processors to: associate each of the knowledge units with a term vector that includes one or more key terms in the corresponding knowledge unit. 18. The system of claim 17, wherein the set of instructions further comprises instructions, which when executed by the one or more processors, causes the one or more processors to: select one of the knowledge units; perform a similarity mapping between the selected knowledge unit and the other knowledge units based on a knowledge unit distance metric computed between the term vector of the selected knowledge unit and the term vector of each of the other knowledge units; identify one or more of the other knowledge units that has the knowledge unit distance metric being below a predetermined threshold distance; and combine the selected knowledge unit and the identified one or more of the other knowledge units into a knowledge pack. 19. The system of claim 15, wherein the set of instructions further comprises instructions, which when executed by the one or more processors, causes the one or more processors to: segment a data file into knowledge segments; and forming one or more of the knowledge units from the knowledge segments. 20. The system of claim 19, wherein the data file is segmented based on organization of content within the data file, or the determined locations of key terms in the data file.