Abstract: Computer-implemented method, apparatus and computer program for automatic analysis of a RDF, Resource Description Framework, dataset. The RDF dataset comprising a set of triples, wherein the RDF dataset is provided as an undirected graph comprising nodes and edges, wherein nodes represent entities and edges represent links between entities.

Abstract: A computer implemented method for keyword search over a knowledge graph. The knowledge graph comprises a large number of vertices representing entities and a large number of edges representing relations between the entities. The knowledge graph is enhanced with static labels. A static label for each vertex includes a list of distances between the vertex and other vertices of the knowledge graph. The method includes receiving a set of keywords, constructing dynamic labels based on the set of keywords and determining a subgraph of the knowledge graph for the set of keywords based on the static labels and based on the dynamic labels. The constructing of the dynamic labels includes obtaining keyword vertices by mapping keywords of the set of keywords to vertices of the knowledge graph and obtaining for the keyword vertices distances between the keyword vertices and predecessors of the keyword vertices from the static labels.

Abstract: A computer-implemented method for keyword search in a data set. Data of the data set is represented by a knowledge graph. The knowledge graph comprises vertices representing entities of the data set and edges representing relations between said entities. The method comprises the following steps: receiving a search query comprising at least two entities; computing for the at least two entities of the search query a salient subset of the data set, wherein the salient subset is computed such that a structural compact subgraph exists in the knowledge graph, the structural compact subgraph connecting the at least two entities of the search query, and computing for the salient subset a structural compact subgraph of the knowledge graph which connects the at least two entities of the search query.

Abstract: A computer-implemented method for predicting a target indicator of a technical system. The method includes: providing a set of data comprising at least data of a first type and at least data of a second type, transforming at least the data of the first type into a first processed subset, transforming at least the data of the second type into a second processed subset, transforming at least the first processed subset and the second processed subset into merged data, and predicting a target indicator of the technical system based on the merged data.

Abstract: A device and computer implemented method. The method includes determining, in a representation of relationships between elements, an element representing a first characteristic of a machine learning pipeline, determining, in the representation, an element representing a second characteristic of the machine learning pipeline depending on the element representing the first characteristic, outputting an output for the element representing the second characteristic, detecting an input, in particular of a user, either determining a parameter of the machine learning pipeline depending on the element representing the second characteristic if the input meets a requirement or not determining the parameter of the machine learning pipeline depending on the element representing the second characteristic otherwise.

Abstract: A computer-implemented method for keyword search in a data set. Data of the data set is represented by a knowledge graph. The knowledge graph comprises vertices representing entities of the data set and edges representing relations between said entities. The method comprises the following steps: receiving a search query comprising at least two entities; computing for the at least two entities of the search query a salient subset of the data set, wherein the salient subset is computed such that a structural compact subgraph exists in the knowledge graph, the structural compact subgraph connecting the at least two entities of the search query, and computing for the salient subset a structural compact subgraph of the knowledge graph which connects the at least two entities of the search query.

Abstract: A computer implemented method for enhancing a knowledge graph with labels, wherein a knowledge graph comprises a large number of vertices representing entities and a large number of edges representing relations between the entities. The method comprises determining a label for each vertex, wherein the label of each vertex comprises a list of distances between said particular vertex and other vertices of the knowledge graph, wherein the distances are sorted in descending order with regard to betweenness centrality of the vertices, starting with a distance to a vertex with the highest number of edges pointing in and out of the vertex.

Abstract: A computer implemented method for keyword search over a knowledge graph. The knowledge graph comprises a large number of vertices representing entities and a large number of edges representing relations between the entities. The knowledge graph is enhanced with static labels. A static label for each vertex includes a list of distances between the vertex and other vertices of the knowledge graph. The method includes receiving a set of keywords, constructing dynamic labels based on the set of keywords and determining a subgraph of the knowledge graph for the set of keywords based on the static labels and based on the dynamic labels. The constructing of the dynamic labels includes obtaining keyword vertices by mapping keywords of the set of keywords to vertices of the knowledge graph and obtaining for the keyword vertices distances between the keyword vertices and predecessors of the keyword vertices from the static labels.

Abstract: A computer-implemented method for processing data for generating data subsets. The method includes: receiving at least one data set that specifies a search result responsive to a search query, the data set including a plurality of data elements and the search query including at least one query term; identifying a number of data elements in said data set, each data element characterized by a weight with regard to coverage of said query terms and/or coverage of a data schema of said data set and/or coverage of key data of said data set, wherein the data elements are identified such that the total weight of the identified data elements is maximized.

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.