Abstract: An approach is provided in which an information handling system detects a multi-entity co-occurrence anomaly within a set of documents that corresponds to an amount of times that a first entity and a second entity co-occur in the set of documents. The information handling system then determines that at least one of the documents includes a title having a verb that grammatically connects the first entity to the second entity. As such, the information handling system collects document segments from the set of documents that have the first entity, the second entity, and the connecting verb. In turn, the information handling system uses the collected document segments to train a relation-based classifier.

Abstract: An approach is provided in which a knowledge manager selects an extraction layer from a convolutional neural network that was trained on an initial set of images. The knowledge manager processes subsequent images obtained from crawling a computer network that includes extracting image feature sets of the subsequent images from the selected extraction layer and generating tags from metadata associated with the subsequent images. In turn, the knowledge manager receives a new image, extracts a new image feature set from the selected extraction layer, and assigns one or more of the tags to the new image based upon evaluating the new image feature set to the image features sets of the subsequent images.

Abstract: An approach is provided in which an information handling system detects a multi-entity co-occurrence anomaly within a set of documents that corresponds to an amount of times that a first entity and a second entity co-occur in the set of documents. The information handling system then determines that at least one of the documents includes a title having a verb that grammatically connects the first entity to the second entity. As such, the information handling system collects document segments from the set of documents that have the first entity, the second entity, and the connecting verb. In turn, the information handling system uses the collected document segments to train a relation-based classifier.

Abstract: An approach is provided in which a knowledge manager selects an extraction layer from a convolutional neural network that was trained on an initial set of images. The knowledge manager processes subsequent images obtained from crawling a computer network that includes extracting image feature sets of the subsequent images from the selected extraction layer and generating tags from metadata associated with the subsequent images. In turn, the knowledge manager receives a new image, extracts a new image feature set from the selected extraction layer, and assigns one or more of the tags to the new image based upon evaluating the new image feature set to the image features sets of the subsequent images.

Abstract: An approach is provided in which a knowledge manager trains a custom taxonomy classifier based upon a set of training samples that results in the custom taxonomy classifier understanding relationships between a set of pre-leaned terms. The knowledge manager then uses the custom taxonomy classifier to analyze input data and determine that the input data corresponds to one or more of the pre-learned terms. In turn, the custom taxonomy classifier matches the corresponding pre-learned terms to user-defined categories and assigns the input data to the matched user-defined categories.

Abstract: An approach is provided in which a knowledge manager selects an extraction layer from a convolutional neural network that was trained on an initial set of images. The knowledge manager processes subsequent images obtained from crawling a computer network that includes extracting image feature sets of the subsequent images from the selected extraction layer and generating tags from metadata associated with the subsequent images. In turn, the knowledge manager receives a new image, extracts a new image feature set from the selected extraction layer, and assigns one or more of the tags to the new image based upon evaluating the new image feature set to the image features sets of the subsequent images.

Abstract: An approach is provided in which a knowledge manager selects an extraction layer from a convolutional neural network that was trained on an initial set of images. The knowledge manager processes subsequent images obtained from crawling a computer network that includes extracting image feature sets of the subsequent images from the selected extraction layer and generating tags from metadata associated with the subsequent images. In turn, the knowledge manager receives a new image, extracts a new image feature set from the selected extraction layer, and assigns one or more of the tags to the new image based upon evaluating the new image feature set to the image features sets of the subsequent images.

Abstract: A method of determining a network event includes copying data communicated across a network and determining the network event using a stub function generated by an end-user.