Open-world modeling
First Claim
1. A system that facilitates generating an inference about a destination of a current trip, comprising:
- a component that makes a log of previously observed events; and
a modeling component, comprising at least one processor, that generates predictions of an event based at least in part upon a combination, the combination being a combination of outputs of;
an open-world submodeling component that provides probabilities that events that are not in the log are likely to occur, the open world submodeling component inferring the probabilities by distributing probabilities over unobserved events; and
a closed-world submodeling component that provides probabilities that events in the log are likely to recur, the closed-world submodeling component inferring the probabilities based on information in the log that indicates probabilities that previously observed events will recur,wherein the modeling component generates a prediction of a future event by combining, for each of a plurality of locations, a probability provided by the open-world submodeling component and a probability provided by the closed-world submodeling component.
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Abstract
The claimed subject matter provides systems and/or methods that facilitate generating an inference about events that may not have yet been observed. Open-world modeling can be used to take a history of observation so as to understand trends over time in the revelation of previously unseen events, and to make inferences with subsets of data that new unseen events will be seen. Thus, inaccuracies associated with predictions generated from incomplete data sets can be mitigated. To yield such predictions, open-world submodels and closed-world submodels that do not allow for previously unseen events can be combined via a model mixture methodology, which fuses inferences from the open- and close-world models.
73 Citations
20 Claims
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1. A system that facilitates generating an inference about a destination of a current trip, comprising:
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a component that makes a log of previously observed events; and a modeling component, comprising at least one processor, that generates predictions of an event based at least in part upon a combination, the combination being a combination of outputs of; an open-world submodeling component that provides probabilities that events that are not in the log are likely to occur, the open world submodeling component inferring the probabilities by distributing probabilities over unobserved events; and a closed-world submodeling component that provides probabilities that events in the log are likely to recur, the closed-world submodeling component inferring the probabilities based on information in the log that indicates probabilities that previously observed events will recur, wherein the modeling component generates a prediction of a future event by combining, for each of a plurality of locations, a probability provided by the open-world submodeling component and a probability provided by the closed-world submodeling component. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A method that facilitates handling incompleteness of data sets to enable generating a prediction, comprising:
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with at least one processor; receiving observed data; computing, based on the observed data, probabilities of previously observed events recurring; and extrapolating the observed data to yield probabilities related to non-observed events; and generating a prediction of an event based at least in part upon a combination, the combination being a combination of the probabilities related to the non-observed events and the previously observed events, wherein the probabilities are weighted in forming the combination by a dynamic factor to place greater emphasis on probabilities related to the previously observed events when the amount of received data increases. - View Dependent Claims (13, 14, 15, 16, 17, 18)
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19. A computer readable storage device comprising computer-executable instructions that, when executed by at least one process perform a method that facilitates generating a prediction, the method comprising:
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generating a closed-world inference based upon observed data, the closed world inference indicating a probability of recurrence of at least one event that has previously been observed; generating an open-world inference by evaluating a probability of observing previously non-observed data over a future period of time; and combining the closed-world inference and the open-world inference based upon a weight to yield the prediction, the weight being based on an amount of observed data. - View Dependent Claims (20)
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Specification