Abstract: One embodiment of the invention provides a method for natural language processing (NLP). The method comprises extracting knowledge outside of text content of a NLP instance by extracting a set of subgraphs from a knowledge graph associated with the text content. The set of subgraphs comprises the knowledge. The method further comprises encoding the knowledge with the text content into a fixed size graph representation by filtering and encoding the set of subgraphs. The method further comprises applying a text embedding algorithm to the text content to generate a fixed size text representation, and classifying the text content based on the fixed size graph representation and the fixed size text representation.

Abstract: Techniques for multi-attribute evaluation of narratives are provided. Inputs are obtained representing: (i) at least one historical dataset of events; (ii) a set of candidate narratives, wherein each candidate narrative is a potential future event sequence; and (iii) a query, wherein the query comprises one or more events of interest to a user. Attribute scores are computed for at least a subset of the candidate narratives based on at least a portion of the obtained input. One of the attribute scores comprises a plausibility attribute score representing a measure estimating the likelihood that a given candidate narrative will occur in the future. Another one of the attribute scores comprises a surprise attribute score representing a measure estimating how surprising a given candidate narrative will be to the user.

Abstract: One embodiment of the invention provides a method for natural language processing (NLP). The method comprises extracting knowledge outside of text content of a NLP instance by extracting a set of subgraphs from a knowledge graph associated with the text content. The set of subgraphs comprises the knowledge. The method further comprises encoding the knowledge with the text content into a fixed size graph representation by filtering and encoding the set of subgraphs. The method further comprises applying a text embedding algorithm to the text content to generate a fixed size text representation, and classifying the text content based on the fixed size graph representation and the fixed size text representation.

Abstract: A method of discovering and presenting associations between events includes discovering causal association scores for pairs of events in an event dataset, and generating a sequence of events based on the causal association scores.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate constrained decision-making and explanation of a recommendation are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a recommendation component that can recommend a decision based on one or more decision policies. The decision can comply with one or more constraints of a constrained decision policy. The computer executable components can further comprise an explanation component that can generate an explanation of the decision. The explanation can comprise one or more factors contributing to the decision.

Abstract: Techniques for multi-attribute evaluation of narratives are provided. Inputs are obtained representing: (i) at least one historical dataset of events; (ii) a set of candidate narratives, wherein each candidate narrative is a potential future event sequence; and (iii) a query, wherein the query comprises one or more events of interest to a user. Attribute scores are computed for at least a subset of the candidate narratives based on at least a portion of the obtained input. One of the attribute scores comprises a plausibility attribute score representing a measure estimating the likelihood that a given candidate narrative will occur in the future. Another one of the attribute scores comprises a surprise attribute score representing a measure estimating how surprising a given candidate narrative will be to the user.

Abstract: Techniques are provided to automatically facilitate a decision process to enable a set of decision makers to reach a decision that represents consensus or near-consensus among the decision makers. The method comprises: obtaining input representing a set of decision alternatives and indicators of desirability corresponding to the decision alternatives; analyzing a degree of consensus among the decision makers in accordance with the desirability indicators obtained; in response to the degree of consensus being deemed sufficient, reporting the decision to the decision makers; otherwise, actively suggesting to the decision makers a set of one or more discussions in which they should engage, and for each of those discussions which decision alternatives should be discussed and which of the decision makers should participate in the discussion; and interacting with the decision makers to facilitate the discussion in order to obtain a degree of consensus that is deemed sufficient.