A system and method for detecting events based on input data from a plurality of sources. The system may receive input from a plurality of sources containing information about possible events. A method for event detection involves pre-processing and normalizing a data input from a plurality of sources, extracting and disambiguating events and entities, associate event and entities, correlate events and entities associated from a data input to results from a different data source to determine if an event has occurred, and store the detected events in a data storage.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method comprising: identifying, by a computer, one or more features in a data stream associated with a data source, wherein at least one feature is an event candidate; automatically determining, by the computer, whether the one or more features identified in the data stream satisfy one or more event models in a categorization table, based upon the computer comparing the one or more features of the data stream against the one or more event models, wherein the event concept store comprises a non-transitory machine-readable memory storing the one or more event models; and responsive to the computer determining that the one or more features from the data stream fail to satisfy at least one event model in at least one categorization table stored in the event concept store: comparing, by the computer, the one or more features against one or more uncategorized event models in an uncategorized event table stored in the event concept store; and storing, by the computer, the one or more features as a new uncategorized event model in the uncategorized event table, in response to determining the one or more features fail to satisfy at least one uncategorized event model. 2. The method according to claim 1, further comprising receiving, by the computer, one or more data streams from one or more data sources. 3. The method according to claim 1, wherein determining whether the one or more features identified in the data stream satisfy at least one of the one or more event models further comprises: automatically comparing, by the computer, the one or more features against one or more event models of one or more categorization tables stored in an event concept store, wherein the event concept store comprises a non-transitory machine-readable memory storing the one or more categorization tables; 4. The method according to claim 3, further comprising: automatically identifying, by the computer, a subset of one or more features from the one or more features, according to an event model; and determining, by the computer, a score for the subset of the one or more features, based on a weight assigned to each respective feature in the subset according to the event model; 5. The method according to claim 4, wherein each respective feature of the subset of features satisfies a threshold value of the event model. 6. The method according to claim 1, further comprising: determining, by the computer, whether the one or more features identified in the data stream satisfy an event model in a categorization table; and automatically identifying, by the computer, in the one or more categorization tables an event model associated with the event candidate, in response to determining that the one or more features satisfy the event model of the one or more event categorization tables. 7. The method according to claim 1, wherein the computer increases a confidence score corresponding to an uncategorized event model when the features are similar to the uncategorized event model. 8. The method according to claim 7, further comprising storing, by the computer, an uncategorized event model as a new event model in the one or more event categorization tables responsive to determining the confidence score corresponding to the uncategorized event model satisfies a threshold score. 9. A system comprising: one or more nodes storing one or more event models, one or more event categorization tables, and an uncategorized event table, wherein each event model is associated with an event candidate and further comprises a threshold event score and a set of one or more features, wherein each event categorization table comprises one or more known event models, and wherein the uncategorized event table comprises a set of one or more features, a set of one or more entities, and a set of one or more topics, each associated with one or more uncategorized event models; and an event category validation processor configured to: receive a set of extracted features, a set of extracted entities, and a set of extracted topics; compare each of the sets with the one or more event categorization tables to determine whether the extracted features, entities, and topics, correspond to a known event model; and then compare each of the sets with the uncategorized event table to determine whether the extracted features, entities, and topics, correspond with an uncategorized event model.