Embodiments of the present invention relate to knowledge representation systems which include a knowledge base in which knowledge is represented in a structured, machine-readable format that encodes meaning. Techniques for extracting structured knowledge from unstructured text and for determining the reliability of such extracted knowledge are also described.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method, comprising: identifying a question and a corresponding answer in unstructured text using one or more computing devices; generating a first fact triple from the question using the one or more computing devices, the first fact triple including an unknown entity object representing an unknown entity; identifying information in the answer corresponding to the unknown entity using the one or more computing devices; identifying a first known entity object corresponding to the information identified in the answer using the one or more computing devices; and replacing the unknown entity object of the first fact triple with the first known entity object to generate a second fact triple using the one or more computing devices. 2. The method of claim 1, wherein the first fact triple includes a second known entity object and a relation object representing a relationship between the unknown entity object and the second known entity object, and wherein generating the first fact triple from the question includes identifying the unknown entity object, the relation object, and the second known entity object from text in the question corresponding to each. 3. The method of claim 2, wherein identifying the unknown entity object, the relation object, and the second known entity object from text in the question includes natural language processing of the unstructured text using one or more of sentence splitting, sentence markup, part-of-speech tagging, sentence parsing, or sentence transformation. 4. The method of claim 1, wherein identifying the information in the answer includes identifying text in the answer corresponding to one of a plurality of entity types. 5. The method of claim 4, wherein identifying the first known entity object includes comparing the text in the answer to one or more text strings associated with each of a plurality of entity objects in a knowledge base, the plurality of entity objects including the first known entity object. 6. The method of claim 1, further comprising retrieving a web page including the unstructured text, wherein identifying the question and the answer in the unstructured text includes natural language processing of the unstructured text. 7. The method of claim 1, further comprising determining a reliability score for the second fact triple, and adding the second fact triple to a knowledge base based on the reliability score. 8. The method of claim 1, further comprising: identifying a second question and a second answer corresponding to the second question from second unstructured text; determining that the second question requires a complex response not suited for representation as a fact triple; and disambiguating one or more entities identified in the second question and referenced in the second answer. 9. The method of claim 1, further comprising: identifying a second question and a second answer corresponding to the second question in second unstructured text; generating a third fact triple from the second question; and determining a polarity of the third fact triple from the answer. 10. A system, comprising one or more computing devices configured to: identify a question and a corresponding answer in unstructured text; generate a fact triple from the question; and determine a polarity of the fact triple from the answer. 11. The system of claim 10, wherein the fact triple includes a first entity object, a second entity object, and a relation object representing a relationship between the first and second entity objects, and wherein the one or more computing devices are configured to generate the fact triple from the question by identifying the relation object and the first and second entity objects from text in the question corresponding to each. 12. The system of claim 11, wherein the one or more computing devices are configured to identify the relation object and the first and second entity objects from text in the question using natural language processing of the unstructured text that includes one or more of sentence splitting, sentence markup, part-of-speech tagging, sentence parsing, or sentence transformation. 13. The system of claim 10, wherein the one or more computing devices are configured to determine a polarity of the fact triple from the answer by identifying positive or negative terms in the answer. 14. The system of claim 10, wherein the one or more computing devices are further configured to retrieve a web page including the unstructured text, and wherein the one or more computing devices are configured to identify the question and the answer in the unstructured text using natural language processing of the unstructured text. 15. The system of claim 10, wherein the one or more computing devices are further configured to determine a reliability score for the second fact triple, and add the second fact triple to a knowledge base based on the reliability score. 16. The system of claim 10, wherein the one or more computing devices are further configured to: identify a second question and a second answer corresponding to the second question from second unstructured text; determine that the second question requires a complex response not suited for representation as a fact triple; and disambiguate one or more entities identified in the second question and referenced in the second answer. 17. The system of claim 10, wherein the one or more computing devices are further configured to: identify a second question and a second answer corresponding to the second question from second unstructured text; generate a second fact triple from the question, the second fact triple including an unknown entity object representing an unknown entity; identify information in the second answer corresponding to the unknown entity; identify a first known entity object corresponding to the information identified in the second answer; and replace the unknown entity object of the second fact triple with the first known entity object to generate a third fact triple. 18. The system of claim 17, wherein the one or more computing devices are configured to identify the information in the second answer by identifying text in the second answer corresponding to one of a plurality of entity types. 19. The system of claim 18, wherein the one or more computing devices are configured to identify the first known entity object by comparing the text in the second answer to one or more text strings associated with each of a plurality of entity objects in a knowledge base, the plurality of entity objects including the first known entity object. 20. A computer program product, comprising one or more computer-readable media having computer program instructions stored therein, the computer program instructions being configured such that, when executed by one or more computing devices, the computer program instructions cause the one or more computing devices to: identify a question and a corresponding answer in unstructured text; generate a first fact triple from the question, the first fact triple including an unknown entity object representing an unknown entity; identify information in the answer corresponding to the unknown entity; identify a first known entity object corresponding to the information identified in the answer; replace the unknown entity object of the first fact triple with the first known entity object to generate a second fact triple; identify a second question and a second answer corresponding to the second question in second unstructured text; generate a third fact triple from the second question; and determine a polarity of the third fact triple from the answer.