Efficient ground truth annotation

A method comprising:

• providing a training data set comprising a set of data samples and a set of target items to be annotated to an annotator;
• receiving the training data set with the annotated target items;
• and
• training a cognitive model of the machine learning application with the training data comprising the annotated target items.

A system comprising one or more processors for executing computer-readable instructions, the computer-readable instructions controlling the one or more processors to perform operations comprising:

• providing a training data set comprising a set of data samples;
• providing an auto-encoder, the auto-encoder encompassing a classifier;
• performing an initial training of an embedding model of the auto-encoder, the embedding model being configured to map the set of data samples to a set of compressed feature vectors comprising feature elements, the set of compressed feature vectors defining a compressed feature matrix of the set of data samples;
• providing a definition of a graph associated to the compressed feature matrix;
• applying a clustering-algorithm to identify one or more node clusters of the graph;
• applying a centrality algorithm to identify one or more central nodes of the one or more node clusters;
• retrieving from an annotator one or more node labels for the one or more central nodes of the one or more node clusters, thereby creating annotated node labels;
• propagating the annotated node labels of the one or more central nodes to other nodes of the graph, thereby creating propagated node labels;
• and
• performing a further training of the auto-encoder, the further training comprising:
• training the embedding model of the auto-encoder and the classifier of the auto-encoder with the annotated and the propagated node labels, wherein the classifier is configured to predict the one or more nodes labels for the elements of the compressed feature vectors.

A computer program product comprising a computer-readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform operations comprising:

• providing a training data set comprising a set of data samples;
• providing an auto-encoder, the auto-encoder encompassing a classifier;
• performing an initial training of an embedding model of the auto-encoder, the embedding model being configured to map the set of data samples to a set of compressed feature vectors comprising feature elements, the set of compressed feature vectors defining a compressed feature matrix of the set of data samples;
• providing a definition of a graph associated to the compressed feature matrix;
• applying a clustering-algorithm to identify one or more node clusters of the graph;
• applying a centrality algorithm to identify one or more central nodes of the one or more node clusters;
• retrieving from an annotator one or more node labels for the one or more central nodes of the one or more node clusters, thereby creating annotated node labels;
• propagating the annotated node labels of the one or more central nodes to other nodes of the graph, thereby creating propagated node labels;
• and
• performing a further training of the auto-encoder, the further training comprising:
• training the embedding model of the auto-encoder and the classifier of the auto-encoder with the annotated and the propagated node labels, wherein the classifier is configured to predict the one or more nodes labels for the elements of the compressed feature vectors.