Abstract: A method for decision-making regarding a decision in an environment by a data processing system in view of multiple different objectives includes: collecting information within the environment, describing the information in at least one temporal knowledge graph (TKG), forecasting a future development of one future state or more future states at a future time or more future points in time, under different decisions by the at least one TKG. The method further describes each resulting future state/decision combination by a corresponding temporal knowledge graph, rates an adherence of each forecasted future state to each objective of the multiple different objectives, considers a trade-off between the objectives for decision-making in a time-aware manner; and provides the decision.

Abstract: A computer-implemented method for predicting links in a temporal knowledge graph (TKG) includes determining one or more anchor nodes and computing, from each node to each anchor node of the TKG for each time-step, relational and temporal paths, and temporal and spatial distances. An embedding is determined for each node to a closest anchor node at each time-step using a vocabulary encoder that combines information received from separate encoders configured to encode the paths and distances. The embedding includes a type of relation. Scores are predicted for each embedding at a future time-step using a scoring function. Link prediction is performed to predict how interaction of the nodes change at the future time-step based on the scores. The present disclosure has applications including, but not limited to, use cases in computational biology, medical AI and healthcare, and cyber threat security for optimizing machine learning processes or supporting decision making.

Abstract: A predictive policing system includes a database of crime related scenarios in a number of past timesteps represented as temporal knowledge graphs (TKGs). Crime prediction devices generate relationship vectors that describe relations for each node of the TKGs for each available timestep. A dataset is created including vector sequences sets for each node of the TKGs, which are used as sequential inputs for training a pattern model to predict future relations for each node of the TKGs. A forecasting model is trained to predict nodes of the TKGs associated with each of the predicted future relations. Predicted future TKGs are assembled describing a crime scenario in an area of interest per future time steps of interest. A forecasting-based action recommendation system computes actions to steer the predicted scenario towards a desired scenario. Monitoring and/or surveillance devices deployed in the area of interest are adapted based on the computed actions.

Abstract: A method for automated machine learning includes controlling execution of a plurality of instantiations of different automated machine learning frameworks on a machine learning task each as a separate arm in consideration of available computational resources and time budget. During the execution by the separate arms, a plurality of machine learning models are trained and performance scores of the plurality of trained machine learning models are computed such that one or more of the plurality of trained machine learning models are selectable for the machine learning task based on the performance scores. This invention can be used for predicting patient discharge, predictive control in buildings for energy optimization, and so on.

Abstract: A method for automated machine learning includes controlling execution of a plurality of instantiations of different automated machine learning frameworks on a machine learning task each as a separate arm in consideration of available computational resources and time budget. During the execution by the separate arms, a plurality of machine learning models are trained and performance scores of the plurality of trained machine learning models are computed such that one or more of the plurality of trained machine learning models are selectable for the machine learning task based on the performance scores. This invention can be used for predicting patient discharge, predictive control in buildings for energy optimization, and so on.

Abstract: A method for automated machine learning includes controlling execution of a plurality of instantiations of different automated machine learning frameworks on a machine learning task each as a separate arm in consideration of available computational resources and time budget. During the execution by the separate arms, a plurality of machine learning models are trained and performance scores of the plurality of trained machine learning models are computed such that one or more of the plurality of trained machine learning models are selectable for the machine learning task based on the performance scores. This invention can be used for predicting patient discharge, predictive control in buildings for energy optimization, and so on.

Abstract: A method for decision-making regarding a decision in an environment by a data processing system in view of multiple different objectives includes: collecting information within the environment, describing the information in at least one temporal knowledge graph (TKG), forecasting a future development of one future state or more future states at a future time or more future points in time, under different decisions by the at least one TKG. The method further describes each resulting future state/decision combination by a corresponding temporal knowledge graph, rates an adherence of each forecasted future state to each objective of the multiple different objectives, considers a trade-off between the objectives for decision-making in a time-aware manner; and provides the decision.

Abstract: A method for automated machine learning includes controlling execution of a plurality of instantiations of different automated machine learning frameworks on a machine learning task each as a separate arm in consideration of available computational resources and time budget. During the execution by the separate arms, a plurality of machine learning models are trained and performance scores of the plurality of trained machine learning models are computed such that one or more of the plurality of trained machine learning models are selectable for the machine learning task based on the performance scores. This invention can be used for predicting patient discharge, predictive control in buildings for energy optimization, and so on.

Abstract: A computer-implemented method for event sequence forecasting of a process instance includes building up and training a three-layered prediction model including a first, a second and a third layer. The first layer is a graph embedding layer that assigns a fixed-dimensional graph embedding vector to each node and relation type in a fused event and knowledge graph that contains available structural information including events, knowledge graph nodes, and links between the events and the knowledge graph nodes. The second layer is an event embedding layer that assigns to each event of the process instance a fixed-dimensional event embedding vector. The third layer is a prediction layer that receives as input a sequence of event embeddings from the second layer and that generates as output a prediction of an unknown property of the event sequence used as input.

Abstract: A method for providing recommendations to users based on a state of interest is provided. The method includes organizing domain of interest information in an initial temporal knowledge graph, wherein t is a timestamp that refers to a present point in time. The method predicts, for at least one future point in time (t+x), future entities, future links between entities and/or future attributes of entities for the initial knowledge graph and produces at least one new knowledge graph based on the predictions, simulates situations resulting from the execution of a particular action or a combination of actions at certain points in time and predicting expected temporal knowledge graphs for the simulated situations, and classifies the knowledge graphs produced for the respective points in time and for the simulated situations based on the state of interest. A ranked list of recommended actions is provided based on the classification result.

Abstract: A method for generating accurate relationships among heterogeneous documents in a semantic graph for an application includes generating a representation for each one of a plurality of heterogeneous documents contained in an expert graph having a plurality of links. A link score is computed for each of the links of at least a first one of the documents based on the representations of the documents. For the first one of the documents, other ones of the documents are selected as link targets based on the link scores using reinforcement learning. The link targets are forwarded to the application.

Abstract: A method for automated machine learning includes controlling execution of a plurality of instantiations of different automated machine learning frameworks on a machine learning task each as a separate arm in consideration of available computational resources and time budget. During the execution by the separate arms, a plurality of machine learning models are trained and performance scores of the plurality of trained machine learning models are computed such that one or more of the plurality of trained machine learning models are selectable for the machine learning task based on the performance scores. This invention can be used for predicting patient discharge, predictive control in buildings for energy optimization, and so on.

Abstract: A method for automatically selecting a machine learning algorithm and tuning hyperparameters of the machine learning algorithm includes receiving a dataset and a machine learning task from a user. Execution of a plurality of instantiations of different automated machine learning frameworks on the machine learning task are controlled each as a separate arm in consideration of available computational resources and time budget, whereby, during the execution by the separate arms, a plurality of machine learning models are trained and performance scores of the plurality of trained models are computed. One or more of the plurality of trained models are selected for the machine learning task based on the performance scores.

Abstract: A method for automatically selecting a machine learning algorithm and tuning hyperparameters of the machine learning algorithm includes receiving a dataset and a machine learning task from a user. Execution of a plurality of instantiations of different automated machine learning frameworks on the machine learning task are controlled each as a separate arm in consideration of available computational resources and time budget, whereby, during the execution by the separate arms, a plurality of machine learning models are trained and performance scores of the plurality of trained models are computed. One or more of the plurality of trained models are selected for the machine learning task based on the performance scores.