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 method for operating at least one trained classifier for measurement data. The classifier comprises a neural network with at least one feature extraction section and at least one classification section. The method includes: processing a record of measurement data with at least the feature extraction section of the classifier; determining a set of neurons in the feature extraction section that are activated by said processing; determining, from a given correspondence between activated neurons and attributes, a set of attributes whose presence in a scene captured by the measurement data is indicated by the activated neurons; comparing attributes to which classes are linked by a given knowledge graph with said determined set of attributes; and evaluating, from the result of this comparison, at least one estimated class as a class to which the scene captured by the record of measurement data is likely to belong.

Abstract: Determining a target's range profiles is an important issue for coastal surveillance radars because it can give us the knowledge about the target, for example, target's type, target's structure and its length along radial direction. Some modern radars nowaday are equipped with the feature of target's range profile extraction, but the results are not accurate due to limitations in processing algorithms. The invention “system and method of determining target's range profiles for coastal surveillance radars” solves the above problem in the direction of proposing a system of technical solutions and associated algorithm improvements.

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: The patent provides the system and the method of evaluation the centroid range-bearing processing in high resolution coastal surveillance radars to solve the problem of assessing the quality of centroid processing. The provided system includes blocks: Input data block, parameter calculation block, evaluation and export result block; The provided method includes steps: Loading input data, calculating parameters, evaluating and exporting results. The system and method provided in this invention solve the issue of the quality assessment of the radar system according to the battle-technical specification at the target centroid level.

Abstract: A device, computer program, computer-implemented method for training a knowledge graph embedding model of a knowledge graph that is enhanced by an ontology. The method comprises training the knowledge graph embedding model with a first training query and its predetermined answer to reduce, in particular minimize, a distance between an embedding of the answer in the knowledge graph embedding model and an embedding of the first training query in knowledge graph embedding model, and to reduce, in particular minimize, a distance between the embedding of the answer and an embedding of a second training query in knowledge graph embedding model, wherein the second training query is determined from the first training query depending on the ontology.

Abstract: A device, computer implemented method, computer program and non-transitory computer-readable storage, for automatically generating negative samples for training a knowledge graph embedding model, The method includes providing at least one first triple, the first triple is a true triple of a knowledge graph, providing at least one second triple, training the knowledge graph embedding model to predict triples of the knowledge graph depending on a set of triples comprising the at least one first triple and the at least one second triple, determining vector representations of entities and relations with the knowledge graph embedding model, determining a plurality of triples with the vector representations of entities and relations, providing an ontology comprising constraints that characterize correct triples, determining with the ontology at least one triple that violates at least one constraint of the constraints or that violates a combination of at least some of the constraints.

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: Determining a target's range profiles is an important issue for coastal surveillance radars because it can give us the knowledge about the target, for example, target's type, target's structure and its length along radial direction. Some modern radars nowaday are equipped with the feature of target's range profile extraction, but the results are not accurate due to limitations in processing algorithms. The invention “system and method of determining target's range profiles for coastal surveillance radars” solves the above problem in the direction of proposing a system of technical solutions and associated algorithm improvements.

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.

Abstract: A computer-implemented method for computing a concept materialization of an ontology is presented whereby a compression technique called “ABox abstraction and refinement” is used that may significantly reduce time, memory, and computing resources for reasoning and in particular for computing and outputting the materialization.

Abstract: A computer-implemented method for computing a concept materialization of an ontology is presented whereby a compression technique called “ABox abstraction and refinement” is used that may significantly reduce time, memory, and computing resources for reasoning and in particular for computing and outputting the materialization.