System for time-efficient assignment of data to ontological classes
Implementations are directed to receiving a set of training data including a plurality of data points, at least a portion of which are to be labeled for subsequent supervised training of a computer-executable machine learning (ML) model, providing at least one visualization based on the set of training data, the at least one visualization including a graphical representation of at least a portion of the set of training data, receiving user input associated with the at least one visualization, the user input indicating an action associated with a label assigned to a respective data point in the set of training data, executing a transformation on data points of the set of training data based on one or more heuristics representing the user input to provide labeled training data in a set of labeled training data, and transmitting the set of labeled training data for training the ML model.
1. A computer-implemented method for providing a visual ensemble labeling (VEL) platform for at least semi-automated labeling of at least a portion of training data, the method being performed by one or more processors, and comprising:
receiving, by the one or more processors, a set of training data comprising a plurality of data points, at least a portion of which are to be labeled for subsequent supervised training of a computer-executable machine learning (ML) model;
providing, by the one or more processors, at least one visualization based on the set of training data, the at least one visualization comprising a graphical representation of at least a portion of the set of training data;
receiving, by the one or more processors, user input associated with the at least one visualization, the user input indicating an action associated with a label assigned to a respective data point in the set of training data;
executing, by the one or more processors, a transformation on data points of the set of training data based on one or more heuristics representing the user input to provide labeled training data in a set of labeled training data; and
transmitting, by the one or more processors, the set of labeled training data for training the ML model.
2. The method of claim 1 , wherein the at least one visualization is provided based on a sparse representation provided from the training data.
3. The method of claim 2 , wherein the sparse representation comprises a sparse matrix.
4. The method of claim 1 , wherein the label is provided at least partially based on a knowledge model comprising a data structure that records an ontology associated with a domain of the ML model.
5. The method of claim 1 , wherein the at least one visualization comprises a two-dimensional data map.
6. The method of claim 1 , wherein the at least one visualization comprises a coincidence grid.
7. The method of claim 1 , wherein the user input comprises at least one search term, and the transformation results in an update to the at least one visualization based on the at least one search term.
8. One or more non-transitory computer-readable storage media coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations for providing a visual ensemble labeling (VEL) platform for at least semi-automated labeling of at least a portion of training data, the operations comprising:
receiving a set of training data comprising a plurality of data points, at least a portion of which are to be labeled for subsequent supervised training of a computer-executable machine learning (ML) model;
providing at least one visualization based on the set of training data, the at least one visualization comprising a graphical representation of at least a portion of the set of training data;
receiving user input associated with the at least one visualization, the user input indicating an action associated with a label assigned to a respective data point in the set of training data;
executing a transformation on data points of the set of training data based on one or more heuristics representing the user input to provide labeled training data in a set of labeled training data; and
transmitting the set of labeled training data for training the ML model.
9. The computer-readable storage media of claim 8 , wherein the at least one visualization is provided based on a sparse representation provided from the training data.
10. The computer-readable storage media of claim 9 , wherein the sparse representation comprises a sparse matrix.
11. The computer-readable storage media of claim 8 , wherein the label is provided at least partially based on a knowledge model comprising a data structure that records an ontology associated with a domain of the ML model.
12. The computer-readable storage media of claim 8 , wherein the at least one visualization comprises a two-dimensional data map.
13. The computer-readable storage media of claim 8 , wherein the at least one visualization comprises a coincidence grid.
14. The computer-readable storage media of claim 8 , wherein the user input comprises at least one search term, and the transformation results in an update to the at least one visualization based on the at least one search term.
15. A system, comprising:
one or more processors; and
a computer-readable storage device coupled to the one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations for providing a visual ensemble labeling (VEL) platform for at least semi-automated labeling of at least a portion of training data, the operations comprising:
receiving a set of training data comprising a plurality of data points, at least a portion of which are to be labeled for subsequent supervised training of a computer-executable machine learning (ML) model;
providing at least one visualization based on the set of training data, the at least one visualization comprising a graphical representation of at least a portion of the set of training data;
receiving user input associated with the at least one visualization, the user input indicating an action associated with a label assigned to a respective data point in the set of training data;
executing a transformation on data points of the set of training data based on one or more heuristics representing the user input to provide labeled training data in a set of labeled training data; and
transmitting the set of labeled training data for training the ML model.
16. The system of claim 15 , wherein the at least one visualization is provided based on a sparse representation provided from the training data.
17. The system of claim 16 , wherein the sparse representation comprises a sparse matrix.
18. The system of claim 15 , wherein the label is provided at least partially based on a knowledge model comprising a data structure that records an ontology associated with a domain of the ML model.
19. The system of claim 15 , wherein the at least one visualization comprises a two-dimensional data map.
20. The system of claim 15 , wherein the at least one visualization comprises a coincidence grid.