Abstract: A transitive closure data structure is constructed for a pair of features represented in a vector space corresponding to an input dataset. The data structure includes a set of entries corresponding to a set of all possible paths between a first feature in the pair and a second feature in the pair in a graph of the vector space. The data structure is reduced by removing a subset of the set of entries such that only a single entry corresponding to a single path remains in the transitive closure data structure. A feature cross is formed from a cluster of features remaining in a reduced ontology graph resulting from the reducing the transitive closure data structure. A layer is configured in a neural network to represent the feature cross, which causes the neural network to produce a prediction that is within a defined accuracy relative to the dataset.

Abstract: A transitive closure data structure is constructed for a pair of features represented in a vector space corresponding to an input dataset. The data structure includes a set of entries corresponding to a set of all possible paths between a first feature in the pair and a second feature in the pair in a graph of the vector space. The data structure is reduced by removing a subset of the set of entries such that only a single entry corresponding to a single path remains in the transitive closure data structure. A feature cross is formed from a cluster of features remaining in a reduced ontology graph resulting from the reducing the transitive closure data structure. A layer is configured in a neural network to represent the feature cross, which causes the neural network to produce a prediction that is within a defined accuracy relative to the dataset.

Abstract: A transitive closure data structure is constructed for a pair of features represented in a vector space corresponding to an input dataset. The data structure includes a set of entries corresponding to a set of all possible paths between a first feature in the pair and a second feature in the pair in a graph of the vector space. The data structure is reduced by removing a subset of the set of entries such that only a single entry corresponding to a single path remains in the transitive closure data structure. A feature cross is formed from a cluster of features remaining in a reduced ontology graph resulting from the reducing the transitive closure data structure. A layer is configured in a neural network to represent the feature cross, which causes the neural network to produce a prediction that is within a defined accuracy relative to the dataset.

Abstract: Identifying private information and preventing privacy violations is provided by a process that evaluates digital information obtained by an organization as part of a digital information stream from a user. The evaluating identifies a user utterance, including keyword(s), entity/ies, and intent(s), and applies natural language understanding to the digital information to ascertain a contextual understanding for the user utterance. The process selects training set(s) of historical information from available training sets that includes vocabulary used in varying contexts. The process compares the identified user utterance to an ontology based on the selected training set(s), and determines a confidence level that the digital information includes digital private information. The process also flags for the organization an action to take with respect to handling of the digital information. The flagging is based on the determined confidence level that the digital information includes digital private information.

Abstract: A transitive closure data structure is constructed for a pair of features represented in a vector space corresponding to an input dataset. The data structure includes a set of entries corresponding to a set of all possible paths between a first feature in the pair and a second feature in the pair in a graph of the vector space. The data structure is reduced by removing a subset of the set of entries such that only a single entry corresponding to a single path remains in the transitive closure data structure. A feature cross is formed from a cluster of features remaining in a reduced ontology graph resulting from the reducing the transitive closure data structure. A layer is configured in a neural network to represent the feature cross, which causes the neural network to produce a prediction that is within a defined accuracy relative to the dataset.

Abstract: Identifying private information and preventing privacy violations is provided by a process that evaluates digital information obtained by an organization as part of a digital information stream from a user. The evaluating identifies a user utterance, including keyword(s), entity/ies, and intent(s), and applies natural language understanding to the digital information to ascertain a contextual understanding for the user utterance. The process selects training set(s) of historical information from available training sets that includes vocabulary used in varying contexts. The process compares the identified user utterance to an ontology based on the selected training set(s), and determines a confidence level that the digital information includes digital private information. The process also flags for the organization an action to take with respect to handling of the digital information. The flagging is based on the determined confidence level that the digital information includes digital private information.

Abstract: Identifying private information and preventing privacy violations is provided by a process that evaluates digital information obtained by an organization as part of a digital information stream from a user. The evaluating identifies a user utterance, including keyword(s), entity/ies, and intent(s), and applies natural language understanding to the digital information to ascertain a contextual understanding for the user utterance. The process selects training set(s) of historical information from available training sets that includes vocabulary used in varying contexts. The process compares the identified user utterance to an ontology based on the selected training set(s), and determines a confidence level that the digital information includes digital private information. The process also flags for the organization an action to take with respect to handling of the digital information. The flagging is based on the determined confidence level that the digital information includes digital private information.