Abstract: A machine learning method for learning and applying a rule set from relational data includes receiving a graph representing relational data, wherein nodes represent elements of the graph, and edges represent relationships between nodes, and generating an intermediate representation of the graph by mapping features of the nodes and edges of the graph to an intermediate vector representation. Optimized logical rules that define the nodes and edges of the graph based on the intermediate vector representation are learned by: defining a maximum satisfiability (MAX-SAT) problem for the graph; and estimating a gradient around a solution of the MAX-SAT problem to produce the optimized logical rules, which are applied to a new graph. The data can be medical data and the graph can be used in a machine-learning task, such as using the medical data for disease prediction, for optimization of the machine-learning task and/or to support decision-making.

Abstract: A machine learning method for learning and applying a rule set from relational data includes receiving a graph representing relational data, wherein nodes represent elements of the graph, and edges represent relationships between nodes, and generating an intermediate representation of the graph by mapping features of the nodes and edges of the graph to an intermediate vector representation. Optimized logical rules that define the nodes and edges of the graph based on the intermediate vector representation are learned by: defining a maximum satisfiability (MAX-SAT) problem for the graph; and estimating a gradient around a solution of the MAX-SAT problem to produce the optimized logical rules, which are applied to a new graph. The data can be medical data and the graph can be used in a machine-learning task, such as using the medical data for disease prediction, for optimization of the machine-learning task and/or to support decision-making.

Abstract: A machine learning method for learning and applying a rule set from relational data includes receiving a graph representing relational data, wherein nodes represent elements of the graph, and edges represent relationships between nodes, and generating an intermediate representation of the graph by mapping features of the nodes and edges of the graph to an intermediate vector representation. Optimized logical rules that define the nodes and edges of the graph based on the intermediate vector representation are learned by: defining a maximum satisfiability (MAX-SAT) problem for the graph; and estimating a gradient around a solution of the MAX-SAT problem to produce the optimized logical rules, which are applied to a new graph. The data can be medical data and the graph can be used in a machine-learning task, such as using the medical data for disease prediction, for optimization of the machine-learning task and/or to support decision-making.

Abstract: A machine learning method for learning and applying a rule set from relational data includes receiving a graph representing relational data, wherein nodes represent elements of the graph, and edges represent relationships between nodes, and generating an intermediate representation of the graph by mapping features of the nodes and edges of the graph to an intermediate vector representation. Optimized logical rules that define the nodes and edges of the graph based on the intermediate vector representation are learned by: defining a maximum satisfiability (MAX-SAT) problem for the graph; and estimating a gradient around a solution of the MAX-SAT problem to produce the optimized logical rules, which are applied to a new graph. The data can be medical data and the graph can be used in a machine-learning task, such as using the medical data for disease prediction, for optimization of the machine-learning task and/or to support decision-making.

Abstract: A machine learning method for learning and applying a rule set from relational data includes receiving a graph representing relational data, wherein nodes represent elements of the graph, and edges represent relationships between nodes, and generating an intermediate representation of the graph by mapping features of the nodes and edges of the graph to an intermediate vector representation. Optimized logical rules that define the nodes and edges of the graph based on the intermediate vector representation are learned by: defining a maximum satisfiability (MAX-SAT) problem for the graph; and estimating a gradient around a solution of the MAX-SAT problem to produce the optimized logical rules, which are applied to a new graph. The data can be medical data and the graph can be used in a machine-learning task, such as using the medical data for disease prediction, for optimization of the machine-learning task and/or to support decision-making.

Abstract: A method for learning logical rules over graph structured data to generate a prediction in a machine learning system includes obtaining graph structured data from a technical application domain of the machine learning system. A graph neural network is trained to learn logical rules using message passing. The prediction is generated in the machine learning system based on the learned logical rules.

Abstract: A machine learning method for learning and applying a rule set from relational data includes receiving a graph representing relational data, wherein nodes represent elements of the graph, and edges represent relationships between nodes, and generating an intermediate representation of the graph by mapping features of the nodes and edges of the graph to an intermediate vector representation. Optimized logical rules that define the nodes and edges of the graph based on the intermediate vector representation are learned by: defining a maximum satisfiability (MAX-SAT) problem for the graph; and estimating a gradient around a solution of the MAX-SAT problem to produce the optimized logical rules, which are applied to a new graph. The data can be medical data and the graph can be used in a machine-learning task, such as using the medical data for disease prediction, for optimization of the machine-learning task and/or to support decision-making.

Abstract: Systems and methods for learning and applying a rule set from relational data include receiving a graph representing relational data, wherein nodes represent elements of the graph, and edges represent relationships between nodes, generating an intermediate representation of the graph by mapping features of the nodes and edges of the graph to an intermediate vector representation, wherein the intermediate vector representation contains binary values and/or probabilistic values, learning logical rules that define the nodes and edges of the graph based on the intermediate vector representation r by: defining a maximum satisfiability (MAX-SAT) problem for the graph; and estimating a gradient around a solution of the MAX-SAT problem for the graph to produce the logical rules; and applying the logical rules to a new graph.

Abstract: A method and system for performing natural language processing is provided to populate improved knowledge graphs. The technique for populating a knowledge graph includes: parsing a text document to extract one or more sentences from the text document; for each sentence in the one or more sentences, identifying a set of concept candidates for the sentence; for each concept candidate in the set of concept candidates, obtaining zero or more compound modifier children of the concept candidate; for each concept candidate and the corresponding compound modifier children, adding a first node to the knowledge graph corresponding to the concept candidate and at least one additional node to the knowledge graph corresponding to the compound modifier children; and adding relations to the knowledge graph to associate the first node with the at least one additional node.

Abstract: Systems and methods for learning and applying a rule set from relational data include receiving a graph representing relational data, wherein nodes represent elements of the graph, and edges represent relationships between nodes, generating an intermediate representation of the graph by mapping features of the nodes and edges of the graph to an intermediate vector representation, wherein the intermediate vector representation contains binary values and/or probabilistic values, learning logical rules that define the nodes and edges of the graph based on the intermediate vector representation r by: defining a maximum satisfiability (MAX-SAT) problem for the graph; and estimating a gradient around a solution of the MAX-SAT problem for the graph to produce the logical rules; and applying the logical rules to a new graph.