Granted Patent B2

US 12,688,366 · App. 17/357,157 · Granted Jul 21, 2026

Inventors: Hanna Wecker (Munich, DE); Annemarie Friedrich (Weil der Stadt, DE); Heike Adel-Vu (Stuttgart, DE)

Assignee: ROBERT BOSCH GMBH

G06F40/30G06F18/23213G06F18/285G06F40/205G06N5/02

Abstract

Claims (39)

1 . A method for filling a knowledge graph, comprising the following steps:

determining a first subset of a set of data points and a second subset of the set of data points;

associating a data point to which a label is assigned with a cluster from among a set of clusters, the data point being associated with the cluster in response to a distribution of labels from data points that are already associated with the cluster satisfies a condition;

associating data points that are associated with the cluster either with the first subset or with the second subset;

training a plurality of models for classification depending on data points from the first subset;

determining, for at least one of the trained models from among the plurality of trained models, a value of a quality factor depending on data points from the second subset;

selecting a trained model for classification from the plurality of trained models depending on the value; and

determining a classification that defines a relationship or a node or a type of node in the knowledge graph for a sentence using the trained model, selected depending on the value, for classification for data.

2 . The method as recited in claim 1 , wherein a number of data points, which are associated with the cluster and with which the label is associated, is determined, the data point being associated with the cluster if the number satisfies a condition, and otherwise not being associated.

3 . The method as recited in claim 2 , wherein a series of data points with which the label is associated is determined, the data points being associated with the cluster in accordance with the series as long as the number of data points satisfies the condition.

4 . The method as recited in claim 3 , wherein the series encompasses the data point, the series being determined depending on a difference between a distance of the data point from a first cluster center of a first cluster from among the set of clusters, and a distance of the data point from a second cluster center of a second cluster from among the set of clusters.

5 . The method as recited in claim 4 , wherein the data point is placed in the series either before another data point if the difference is greater than a reference, or otherwise after the other data point in the series.

6 . The method as recited in claim 4 , wherein the first cluster center is that cluster center, from among the set of cluster centers, which is located closest to the data point, the second cluster center being that cluster center, from among the set of cluster centers, which is located farthest from the data point.

7 . The method as recited in claim 6 , further comprising:

determining a third cluster center depending on data points that are associated with the first cluster;

determining a fourth cluster center depending on data points that are associated with the second cluster;

determining a first distance of a first data point of the first cluster from the third cluster center, and a second distance of the first data point from the fourth cluster center;

determining a third distance of a second data point of the second cluster from the fourth cluster center, and a fourth distance of the second data point from the third cluster;

determining a first difference between the first distance and the second distance;

determining a second difference between the third distance and the fourth distance; and

based on the labels of the first and second data points being the same, and based on the first difference satisfying a first condition and the second difference satisfying a second condition, associating the first data point with the second cluster, and the second data point with the first cluster.

8 . The method as recited in claim 1 , wherein a plurality of cluster centers is furnished, data points from among the set of data points being associated with one of the cluster centers from among the plurality of cluster centers.

9 . The method as recited in claim 8 , wherein a number of subsets for the data points is predefined, the number of subsets defining a number of cluster centers.

10 . An apparatus configured to fill a knowledge graph, the apparatus configured to:

determine a first subset of a set of data points and a second subset of the set of data points;

associate a data point to which a label is assigned with a cluster from among a set of clusters, the data point being associated with the cluster in response to a distribution of labels from data points that are already associated with the cluster satisfies a condition;

associate data points that are associated with the cluster either with the first subset or with the second subset;

train a plurality of models for classification depending on data points from the first subset;

determine, for at least one of the trained models from among the plurality of trained models, a value of a quality factor depending on data points from the second subset;

select a trained model for classification from the plurality of trained models depending on the value; and

determine a classification that defines a relationship or a node or a type of node in the knowledge graph for a sentence using the trained model, selected depending on the value, for classification for data.

11 . A non-transitory computer-readable medium on which is stored a computer program including computer-readable instructions for filling a knowledge graph, the instructions, when executed by a computer, causing the computer to perform the following steps:

determining a first subset of a set of data points and a second subset of the set of data points;

associating a data point to which a label is assigned with a cluster from among a set of clusters, the data point being associated with the cluster in response to a distribution of labels from data points that are already associated with the cluster satisfies a condition;

associating data points that are associated with the cluster either with the first subset or with the second subset;

training a plurality of models for classification depending on data points from the first subset;

determining, for at least one of the trained models from among the plurality of trained models, a value of a quality factor depending on data points from the second subset;

selecting a trained model for classification from the plurality of trained models depending on the value; and

determining a classification that defines a relationship or a node or a type of node in the knowledge graph for a sentence using the trained model, selected depending on the value, for classification for data.

Assignments (1)

Priority Claims (1)

Continuity (1)

References Cited (21)