Robert Boschgmbh

Heike Adel-Vu

Jannik Stroetgen

Lukas Lange

Daria Stepanova

Mohamed Gad-Elrab

Trung Kien Tran

Anastasiia Iurshina

Annemarie Friedrich

Johannes Christoph Hingerl

A device and method for determining a knowledge graph. A second embedding is determined for a first embedding for a word including a function. A first classification, which determines whether or not the word is an entity for the knowledge graph, or which defines to which entity or to which type of entity for the knowledge graph the word in the knowledge graph is to be assigned, is determined for the second embedding using a first classifier. A second classification, which defines to which type of embeddings from a plurality of types of embeddings the second embedding is to be assigned, is determined for the second embedding using a second classifier. At least one parameter for the function is trained in a training as a function of a gradient for the training of the first classifier and as a function of a gradient for the training of the second classifier.

Device and method for determining a knowledge graph

A device for the automatic analysis of multilingual text, including an embedder, which is configured for assigning a numeric representation to each of the text components from the multilingual text, and a temporal tagger, which is configured for identifying and tagging temporal expressions in the multilingual text depending on the assigned numeric representations. The embedder is configured for assigning the numeric representations of temporal expressions in such a way that it is not possible to ascertain, on the basis of the numeric representation, in which language the associated text component is written.

Device for an automated generation of a knowledge graph

A computer-implemented method for grouping target entities into clusters. A base association in which a cluster is associated with each of the target entities is determined in a computation step for the target entities as a function of an association for entities. Inference rules are determined as a function of the association for entities and as a function of the base association, each of the inference rules defining an association of entities with one of the clusters. An altered association is determined as a function of the association for entities and the inference rules. A check is made as to whether a difference between the base association and the altered association falls below a threshold value. When it does, an association of the target entities with the clusters is output or stored. Otherwise, a feedback value is determined as a function of the difference.

Device and method for machine learning

A device and method for determining at least one part of a knowledge graph. A body of text is made available; for one sentence from the body of text, a first, second, and third input respectively for a first, second, and third classifier is determined. Each of the first, second, and third inputs includes a numerical representation of at least one part of the sentence. A numerical representation of a first probability is determined by the first classifier as a function of the first input, which indicates whether or not the sentence relates to the knowledge graph. If the numerical representation of the first probability satisfies a first condition, a numerical representation of a second probability is determined by the second classifier as a function of the second input, which defines a first type for the word from the sentence.

Device and method for determining at least one part of a knowledge graph

A computer-implemented method for processing text data including a multitude of text modules. In the method, a representation of the text is provided, and a model is used which predicts a classification for a respective text module of the text as a function of the representation of the text. The provision of the representation of the text includes the provision of a total word vector for a respective text module of the text. The total word vector is formed from at least two, preferably multiple word vectors, and a respective word vector being weighted as a function of properties of the respective text module.

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Stuttgart, Germany

Heike Adel-Vu