A system for building a knowledge base of co-occurring features extracted from a document corpus is disclosed. The method includes a plurality of feature extraction software modules that may extract different features from each document in the corpus. The system may include a knowledge base aggregator module that may keep count of the co-occurrences of features in the different documents of a corpus and determine appropriate co-occurrences to store in a knowledge base.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: crawling, via an entity extraction computer, a corpus of electronic documents; extracting, via the entity extraction computer, a plurality of features from each of the crawled documents in the corpus; aggregating, via a knowledge base aggregator computer, instances of co-occurrence of two or more of the plurality of features across the crawled documents to determine a count of the instances of co-occurrence; and adding, via the knowledge base aggregator computer, an instance of co-occurrence of the two or more features to a feature co-occurrence database when the count of the instances of co-occurrence exceeds a predetermined threshold. 2. The method of claim 1 wherein each of the plurality of features is selected from the group consisting of a person, a location, an organization name, a topic, an event, and a fact. 3. The method of claim 1 further comprising adding, via the knowledge base aggregator computer module, the instance of the co-occurrence of the two or more features to the feature co-occurrence database along with metadata pertaining to the instance. 4. The method of claim 3 wherein the metadata is selected from the group consisting of feature type, document identifier, document corpus identifier, distance in text between co-occurring features, and a confidence score. 5. The method of claim 3 where in the metadata includes a confidence score indicative whether the two or more of the plurality of features associated with a respective instance of co-occurrence are unique. 6. The method of claim 5 wherein the confidence score is calculated using a plurality of parameters including a number of co-occurrences in a single document, a number of co-occurrences in the corpus of electronic documents, a size of the corpus of electronic documents, a number of co-occurrences in different corpora of electronic documents, distance in text from co-occurring features, and an indicator of human verification. 7. A system comprising: an entity extraction computer configured to crawl a corpus of electronic documents and extract a plurality of features from each of the crawled documents in the corpus; and a knowledge base aggregator computer configured to aggregate instances of co-occurrence of two or more of the plurality of features across the crawled documents to determine a count of the instances of co-occurrence, and add an instance of co-occurrence of the two or more features to a feature co-occurrence database when the count of the instances of co-occurrence exceeds a predetermined threshold. 8. The system of claim 7 wherein each of the plurality of features is selected from the group consisting of a person, a location, an organization name, a topic, an event, and a fact. 9. The system of claim 7 wherein the knowledge aggregator computer is further configured to add the instance of the co-occurrence of the two or more features to the feature co-occurrence database along with metadata pertaining to the instance. 10. The system of claim 9 wherein the metadata is selected from the group consisting of feature type, document identifier, document corpus identifier, distance in text between co-occurring features, and a confidence score. 11. The system of claim 9 where in the metadata includes a confidence score indicative whether the two or more of the plurality of features associated with a respective instance of co-occurrence are unique. 12. The system of claim 11 wherein the knowledge base aggregator computer is further configured to calculate the confidence score using a plurality of parameters including a number of co-occurrences in a single document, a number of co-occurrences in the corpus of electronic documents, a size of the corpus of electronic documents, a number of co-occurrences in different corpora of electronic documents, distance in text from co-occurring features, and an indicator of human verification. 13. A non-transitory computer readable medium having stored thereon computer executable instructions comprising: crawling, via an entity extraction computer, a corpus of electronic documents; extracting, via the entity extraction computer, a plurality of features from each of the crawled documents in the corpus; aggregating, via a base aggregator computer, instances of co-occurrence of two or more of the plurality of features across the crawled documents to determine a count of the instances of co-occurrence; and adding, via the base aggregator computer, an instance of co-occurrence of the two or more features to a feature co-occurrence database when the count of the instances of co-occurrence exceeds a predetermined threshold. 14. The computer readable medium of claim 13 wherein each of the plurality of features is selected from the group consisting of a person, a location, an organization name, a topic, an event, and a fact. 15. The computer readable medium of claim 13 wherein the instructions further comprise adding, via the entity extraction computer, the instance of the co-occurrence of the two or more features to the feature co-occurrence database along with metadata pertaining to the instance. 16. The computer readable medium of claim 15 wherein the metadata is selected from the group consisting of feature type, document identifier, document corpus identifier, distance in text between co-occurring features, and a confidence score. 17. The computer readable medium of claim 15 where in the metadata includes a confidence score indicative whether the two or more of the plurality of features associated with a respective instance of co-occurrence are unique. 18. The computer readable medium of claim 17 wherein the instructions further comprise calculating, via the knowledge base aggregator computer, the confidence score using a plurality of parameters including a number of co-occurrences in a single document, a number of co-occurrences in the corpus of electronic documents, a size of the corpus of electronic documents, a number of co-occurrences in different corpora of electronic documents, distance in text from co-occurring features, and an indicator of human verification.