Abstract: Apparatus and computer-implemented method for correcting inconsistent facts in a knowledge base. The method comprises providing an inconsistent fact, wherein the inconsistent fact comprises a subject and a predicate and an object, determining an input for a language model, wherein the input comprises the subject or a label provided for the subject, wherein the input comprises the predicate or a label provided for the predicate, wherein the object or a label provided for the object is masked in the input, determining an output of the language model depending on the input, wherein the output comprises a predicted object or a predicted label for a predicted object, and replacing the inconsistent fact with a fact comprising the subject, the predicate and the predicted object.

Abstract: A device and method for training a model for linking a mention in textual context to an entity across knowledge bases. I the method, depending on training data, training the model for mapping an entity of a first knowledge base to its first representation in a vector space, for mapping an entity of a second knowledge base to its second representation in the vector space, for mapping the mention to a third representation in the vector space. The training data includes a set of pairs in which each pair includes a mention in a textual context and its corresponding reference entity in either the first knowledge base or the second knowledge base. Training the model includes evaluating a loss function.

Abstract: A device and a computer implemented method for explainable clustering of a scene. The method includes determining a first relation that relates a first object class to a second object class, wherein determining the first relation includes determining, depending on the first object class and the second object class, a pair of entities in a first knowledge graph, in particular a commonsense knowledge graph, that represents information about a domain, wherein the pair of entities is related with the first relation in the first knowledge graph, determining a cluster in that the scene belongs depending on the scene and depending on other scenes, determining a second relation that relates the scene with the cluster depending on at least one feature of digital image data representing the scene, determining a rule that maps the first relation to the second relation.

Abstract: A method for determining negative samples for training a knowledge graph embedding of a knowledge graph enhanced by an ontology including at least one constraint for distinguishing a fact of the knowledge graph from a spurious fact. The method comprises determining embedding predicted triples; determining a set of triples that comprises a triple of the knowledge graph and at least one of the predicted triples that are inconsistent with respect to the ontology; determining from the set of triples a replacement entity for the object entity in the at least one triple of the predicted triples; and determining the negative sample to comprise the relation, the subject entity and the replacement entity, or determining from the subset a replacement entity for the subject entity in the at least one triple of the predicted triples and determining the negative sample to comprise the relation, the object entity, and the replacement entity.

Abstract: A device and computer implemented method. The method includes determining an embedding of a first entity, in particular of a knowledge graph, inserting a first vertex for the embedding in an in particular weighted in particular undirected graph, determining in the graph a first cluster of vertices including the first vertex, determining for the first cluster a second entity, in particular in the knowledge graph, determining a semantic similarity between the first entity and the second entity, in particular in the knowledge graph, determining a rule for the first cluster depending on the semantic similarity between the first entity and the second entity.

Abstract: 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.

Abstract: A computer-implemented method for computing inconsistency explanations in a first data set, enhanced with an ontology, the first data set comprising data elements, called individuals, and facts about the individuals; the facts are expressed according to an ontology language in terms of class assertions and/or property assertions, a class assertion relates one individual with a class and a property assertion relates one individual with a second individual. The ontology includes a formal explicit description of the classes and/or properties and further including axioms about the classes and/or properties; wherein the method includes the steps of: constructing a second data set being an abstract description of the first data set; computing inconsistency explanations in the second data set with regard to the axioms of the ontology, and computing inconsistency explanations for the first data set with regard to the ontology based on the computed inconsistency explanations in the second data set.