Method and system for detecting semantic events
First Claim
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1. A method for detecting a semantic temporal event, said method comprising:
- retrieving multiple-layer models corresponding to said semantic temporal event;
receiving temporal observations that are extracted, from at least one data source, according to said multiple-layer models for the semantic temporal event;
detecting one or more occurrences of the semantic temporal event based on said temporal observations and said multiple-layer models by supplying said temporal observations to said multiple-layer models;
characterizing said one or more occurrences of the semantic temporal event, detected by said detecting, to produce a characterization;
storing said characterization;
performing temporal event prediction based on said characterization;
revising said multiple-layer models for said semantic temporal event based on said characterization; and
simulating parts of said semantic temporal event according to said characterization.
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Abstract
A method and system is provided for detecting occurrences of semantic temporal events based on observations extracted from input data and event models. The input data is fed into the system from some data source. Based on specified event to be detected, multiple-layer models corresponding to the event are retrieved. The models are used to determine the types of temporal observations to be extracted from the input data. The extracted temporal observations are then used, in combination with the multiple-layer models of the event, to detect the occurrences of the event.
104 Citations
20 Claims
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1. A method for detecting a semantic temporal event, said method comprising:
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retrieving multiple-layer models corresponding to said semantic temporal event;
receiving temporal observations that are extracted, from at least one data source, according to said multiple-layer models for the semantic temporal event;
detecting one or more occurrences of the semantic temporal event based on said temporal observations and said multiple-layer models by supplying said temporal observations to said multiple-layer models;
characterizing said one or more occurrences of the semantic temporal event, detected by said detecting, to produce a characterization;
storing said characterization;
performing temporal event prediction based on said characterization;
revising said multiple-layer models for said semantic temporal event based on said characterization; and
simulating parts of said semantic temporal event according to said characterization. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
performing semantic temporal event detection using a plurality of detection methods, each of said plurality of detection methods producing a detection result; and
combining said detection results with each other to produce a final detection.
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11. The method according to claim 10, wherein said plurality of detection methods includes dynamic Bayesian networks, rule based expert systems, decision trees, Hidden Markov Models, neural networks, or fuzzy logic.
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12. The method according to claim 1, wherein said characterization includes:
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a set of statistics computed from said one or more occurrences;
ora set of descriptions, each of which describes an action happening in an occurrence of the semantic temporal event.
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13. A computer-readable medium for programming a computer to detect a semantic temporal event, comprising instructions for:
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retrieving multiple-layer models corresponding to said semantic temporal event;
receiving temporal observations that are extracted, from at least one data source, according to said multiple-layer models for the semantic temporal event;
detecting one or more occurrences of the semantic temporal event based on said temporal observations and said multiple-layer models;
characterizing said one or more occurrences of the semantic temporal event, detected by said detecting, to produce a characterization;
storing said characterization;
performing temporal event prediction based on said characterization;
revising said multiple-layer models based on said characterization; and
simulating parts of said semantic temporal event according to said characterization. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20)
performing semantic temporal event detection using a plurality of detection methods, each of said plurality of detection methods producing a detection result; and
combining said detection results with each other to produce a detection.
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19. The computer-readable medium according to claim 18, wherein said plurality of detection methods includes dynamic Bayesian networks, rule based expert systems, decision trees, Hidden Markov Models, neural networks, or fuzzy logic.
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20. The computer-readable medium according to claim 13, wherein said characterization includes:
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a set of statistics computed from said one or more occurrences;
ora set of descriptions, each of which describes an action happening in an occurrence of the semantic temporal event.
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Specification