Abstract: Various examples are directed to systems and methods for generating a query of a database. An example method may comprise accessing a knowledge base data structure comprising plurality of nodes and a plurality of edges and accessing a plurality of training samples comprising a plurality of positive training samples and a plurality of negative training samples. The example method may also comprise determining a subgraph comprising a subset of the plurality of nodes and a subset of the plurality of edges, the subset of the plurality of nodes comprising at least two nodes that are also part of the plurality of training samples and executing a subgraph query of the knowledge base data structure, the subgraph query being based at least in part on the subgraph.

Abstract: Various examples are directed to systems and methods in an analytics computing system. The analytics computing system may receive query data and an indication of a subject tuple. The analytics computing system may access analytics code comprising first proposition data corresponding to the subject tuple. The first proposition data may comprise proposition head data describing the subject tuple and proposition body data describing at least one proposition condition, the at least one proposition condition comprising a relationship between a first proposition body tuple and a second proposition body tuple. The analytics computing system may access query processing data and determine a subset of the query processing data for which the at least one proposition condition is true.

Abstract: Various examples are directed to systems and methods for processing queries against a process model. An analytics computing system may access query data describing a query to an analytics computing system. The analytics computing system may access process model code that comprises first function code for evaluating a function having a first input and a second input. The first function code may indicate that the first input is at a first time context corresponding to a first discrete time. And that the second input is at a second time context corresponding to a second discrete time period adjacent the first discrete time period. The analytics computing system may execute the process model code using a value for a first input at the first discrete time period and a value for a second input at the second discrete time period.

Abstract: Provided are a computer-implemented method, a computer program and a system for determining a knowledge graph query having a response conforming to a plurality of training samples. The method may comprise receiving a knowledge graph; receiving the training samples, wherein each of the training samples is a relation, wherein each value in the training samples corresponds to one of the nodes of the knowledge graph, wherein the training samples include one or more positive training samples and one or more negative training samples; identifying one or more variables from the training samples.

Abstract: A computer-implemented method comprises: receiving trigger data comprising at least one trigger concept; retrieving at least one rule based on the trigger data, wherein a rule comprises: a main label relating two concept variables and a concatenated set of defining labels, each defining label relating two concept variables; obtaining problem data by querying a knowledge graph using the at least one rule and the at least one trigger concept, wherein the knowledge graph comprises a plurality of nodes and a plurality of edges, each node being associated with a respective concept and each edge being associated with a respective label, and wherein at least one node is associated with the at least one trigger concept; obtaining solution data based on the problem data; performing an action based on the solution data.

Abstract: Provided are a computer-implemented method, a computer program and a system for determining a knowledge graph query having a response conforming to a plurality of training samples. The method may comprise receiving a knowledge graph; receiving the training samples, wherein each of the training samples is a relation, wherein each value in the training samples corresponds to one of the nodes of the knowledge graph, wherein the training samples include one or more positive training samples and one or more negative training samples; identifying one or more variables from the training samples.

Abstract: A system is provided, which may comprise: a word vector database configured to store word vectors, each of the word vectors representing a word or a phrase; a text database configured to store text data; and a processor configured to: receive a user input including a first term; obtain, for each word vector that is stored in the word vector database and that represents a word or a phrase other than the first term, a similarity between that word vector and a word vector representing the first term; select at least one second term based on the obtained similarity; perform a search in the text database using the at least one second term as a query for the search; and provide results of the search.

Abstract: A computer-implemented method is provided for providing an interface between a frontend application configured to receive one or more user inputs in a natural language and a backend system configured to provide a service to a user.

Abstract: A database querying system, comprising an input interface unit configured to receive textual input and to convert the textual input into a textual data string, a sequencing unit configured to obtain at least one key phrase from the textual data string and to obtain for each of the at least one key phrase a set of all contiguous sequences of words of the respective key phrase, a classification network unit configured to access a semantic network for each of the contiguous sequences of words, wherein the classification network unit is configured to set a classification flag for the respective contiguous sequence of words, if the semantic network contains a link between the respective contiguous sequence of words and an entry in a predetermined classification database; a comparison unit configured to access a predetermined target database for each of the contiguous sequences of words, wherein the comparison unit is configured to set a target flag for the respective contiguous sequence of words, if the predetermi

Abstract: A converter system for multi-component systems, comprising a multi-component system adjustment module, includes: an input/output unit, an adjustment unit configured to adjust at least one primary component of a primary multi-component system for each of the at least one primary component given as a probability distribution, a certainty evaluation unit configured to evaluate a certainty parameter of the primary multi-component system, and compare the certainty parameter with a certainty threshold, and a prompt selection unit configured to, if the certainty parameter of the primary multi-component system does not meet the certainty threshold, select a further primary component prompt of the plurality of primary component prompts and instructing the multi-component system adjustment module to perform the above steps on the basis of the further primary component prompt; wherein the converter system further comprises a multi-component system converter module, comprising: a converter unit configured to generate a p

Abstract: A database querying system, comprising an input interface unit configured to receive textual input and to convert the textual input into a textual data string, a sequencing unit configured to obtain at least one key phrase from the textual data string and to obtain for each of the at least one key phrase a set of all contiguous sequences of words of the respective key phrase, a classification network unit configured to access a semantic network for each of the contiguous sequences of words, wherein the classification network unit is configured to set a classification flag for the respective contiguous sequence of words, if the semantic network contains a link between the respective contiguous sequence of words and an entry in a predetermined classification database; a comparison unit configured to access a predetermined target database for each of the contiguous sequences of words, wherein the comparison unit is configured to set a target flag for the respective contiguous sequence of words, if the predetermi

Abstract: A converter system for multi-component systems, comprising a multi-component system adjustment module, includes: an input/output unit, an adjustment unit configured to adjust at least one primary component of a primary multi-component system for each of the at least one primary component given as a probability distribution, a certainty evaluation unit configured to evaluate a certainty parameter of the primary multi-component system, and compare the certainty parameter with a certainty threshold, and a prompt selection unit configured to, if the certainty parameter of the primary multi-component system does not meet the certainty threshold, select a further primary component prompt of the plurality of primary component prompts and instructing the multi-component system adjustment module to perform the above steps on the basis of the further primary component prompt; wherein the converter system further comprises a multi-component system converter module, comprising: a converter unit configured to generate a p

Abstract: A system is provided, which may comprise: a word vector database configured to store word vectors, each of the word vectors representing a word or a phrase; a text database configured to store text data; and a processor configured to: receive a user input including a first term; obtain, for each word vector that is stored in the word vector database and that represents a word or a phrase other than the first term, a similarity between that word vector and a word vector representing the first term; select at least one second term based on the obtained similarity; perform a search in the text database using the at least one second term as a query for the search; and provide results of the search.

Abstract: A computer-implemented method is provided for providing an interface between a frontend application configured to receive one or more user inputs in a natural language and a backend system configured to provide a service to a user.