Abstract: In one embodiment of the present invention, a semantic analyzer translates a text segment into a structured representation that conveys the meaning of the text segment. Notably, the semantic analyzer leverages a semantic network to perform word sense disambiguation operations that map text words included in the text segment into concepts—word senses with a single, specific meaning—that are interconnected with relevance ratings. A topic generator then creates topics on-the-fly that includes one or more mapped concepts that are related within the context of the text segment. In this fashion, the topic generator tailors the semantic network to the text segment. A topic analyzer processes this tailored semantic network, generating a relevance-ranked list of topics as a meaningful proxy for the text segment. Advantageously, operating at the level of concepts and topics reduces the misinterpretations attributable to key word and statistical analysis methods.

Abstract: In one embodiment of the present invention, a semantic analyzer translates a text segment into a structured representation that conveys the meaning of the text segment. Notably, the semantic analyzer leverages a semantic network to perform word sense disambiguation operations that map text words included in the text segment into concepts—word senses with a single, specific meaning—that are interconnected with relevance ratings. A topic generator then creates topics on-the-fly that includes one or more mapped concepts that are related within the context of the text segment. In this fashion, the topic generator tailors the semantic network to the text segment. A topic analyzer processes this tailored semantic network, generating a relevance-ranked list of topics as a meaningful proxy for the text segment. Advantageously, operating at the level of concepts and topics reduces the misinterpretations attributable to key word and statistical analysis methods.

Abstract: A method, system and computer memory for optimally de-identifying a dataset is provided. The dataset from a storage device. The equivalence classes within the dataset is determined. A lattice is determined defining anonymization strategies. A solution set for the lattice is generated. Optimal node from the solution set is determined. The dataset is then de-identified using the generalization defined by the optimal node and can then be stored on the storage device.

Abstract: A method, system and computer memory for optimally de-identifying a dataset is provided. The dataset from a storage device. The equivalence classes within the dataset is determined. A lattice is determined defining anonymization strategies. A solution set for the lattice is generated. Optimal node from the solution set is determined. The dataset is then de-identified using the generalization defined by the optimal node and can then be stored on the storage device.