Abstract: A solution for automated column type annotation maps each column contained in a table to a column annotation class. A pre-processor transforms the table into a numerical tensor representation by outputting a sequence of cell tokens for each cell in the table. A table encoder encodes the sequences of cell tokens and a column annotation label for each column into body cell embeddings. A body pooling component processes the body cell embeddings to provide column representations. A classifier classifies the column representations to provide for each column, confidence scores for each column annotation class. The method concludes with comparing the highest confidence score for each column with a threshold, and, if the highest confidence score for each column is above the threshold, annotating each column with the respective column annotation class.

Abstract: One or more example embodiments relates to a computer-implemented method for determining a similarity measure, the similarity measure describing a similarity between a first patient and a second patient. The method includes receiving a first patient data record, wherein the first patient data record is assigned to the first patient; receiving a second patient data record, wherein the second patient data record is assigned to the second patient; receiving or determining a medical ontology, wherein the medical ontology is independent of the first patient data record and the second patient data record; determining a patient ontology based on the medical ontology and at least one of the first patient data record or the second patient data record; and determining the similarity measure based on the patient ontology.

Abstract: For detecting sensor anomalies, a machine learning model models a material flow in an industrial system, as a hierarchical time series, wherein the hierarchical time series represents a structure of the material flow using a directed acyclic graph with a set of nodes and a set of edges, wherein each node is associated to a time series, and wherein the edges represent parent-child relations where each value of a time series at a parent node equals the sum of the respective values of its child nodes. The machine learning model forecasts predicted time series values for all nodes. Current sensor measurements received from sensors placed in the industrial system are compared to the predictions of the machine learning model. An anomaly is detected if the difference exceeds a threshold.

Abstract: For anomaly detection in a network, a temporal knowledge graph represents the network including interactions between network modules with a set of entities, a set of relations, and a set of timestamps. In a first step, temporal random walks are sampled from the temporal knowledge graph. These are transformed in a second step into temporal logical rules. After observing an event in the network—or in a different network—the observed event is classified in a third step regarding an anomaly, using the temporal logical rules. The temporal knowledge graph is used as a stream-based data structure to extract rules that identify typical temporal behavior of the network and is used to identify anomalies in a human-interpretable way. The anomaly detection task is framed as a quadruple classification problem, using the temporal logical rules and their respective groundings in the temporal knowledge graph to support the classification.

Abstract: A recommender system to be used in the context of an engineering tool is provided. By using the recommender, a list of items is provided in the engineering tool which are likely to be connected in a next step to an engineering project designed in the engineering tool.

Abstract: Based on a graph storing a current state of an engineering project consisting of modules, a graph neural network computes an embedding for each node. For each node embedding, a classifier determines a preliminary confidence score for each class, which represents a type of module that could be added to the engineering project. A topology-based measure is calculated at least for a current center node. A blank node is assigned to a bin depending on the topology-based measure that has been computed for the current center node. A post-processor calibrates all preliminary confidence scores for the blank node by applying a scaling factor depending on the assigned bin. Finally, a user interface outputs at least the class with the highest calibrated confidence score for the blank node as well as the respective calibrated confidence score. The binning scheme takes the graph structure into account and allows for adaptive calibration.

Abstract: A database stores a set of items, with each item having technical attributes, and with each item representing a module that can be used in an engineering project of a first user, u1. A feature encoder embeds each item based on its technical attributes into a low-dimensional vector space. Then, in a second step, a graph neural network pools over these item embeddings to compute an updated user embedding for the first user A decoder mapping then addresses the recommendation task by outputting recommendation scores for each item. That means, heuristically speaking, that the method and system lift the recommendation task to the level of technical attributes to overcome the sparsity problem caused by item sets that are not overlapping between user groups. Thus, when matching similar users, the method does not rely on users configuring exactly the same modules but rather on configured modules that are similar from a technical point of view.

Abstract: A method and system for automatic maintenance of a machine (2) comprising the steps of receiving (S1) at least one maintenance relevant event (E) from a controller (3) of the machine (2); augmenting (S2) the received event (E) with the event's machine context read from a machine maintenance ontology; matching (S3) the event's machine context with maintenance rules to generate at least one maintenance task (T) comprising an associated task description; and providing (S4) a maintenance schedule for the machine (2) assigning the generated maintenance task (T) to suitable maintenance executing entities (5) on the basis of the task description of the respective maintenance task (T).

Abstract: A machine learning model processes a current partial design of a technical system and a candidate component for a next design step of designing the technical system. The model computes a probability distribution, which is a probability distribution over changes of a design KPI if the candidate component is added to the current partial design, with the design KPI describing a property of the technical system, and a predicted impact value predicting an absolute value of the design KPI or a change of the design KPI if the candidate component is added to the current partial design. These predictions (for partial designs that cannot be processed by a simulation environment due to their incompleteness) can drastically shorten the feedback loop between engineers in charge of designing a new technical system/product and a simulation environment used for estimating the performance characteristics of the product.

Abstract: An initially trained machine learning model is used by an active learning module to generate candidate triples, which are fed into an expert system for verification. As a result, the expert system outputs novel facts that are used for retraining the machine learning model. This approach consolidates expert systems with machine learning through iterations of an active learning loop, by bringing the two paradigms together, which is in general difficult because training of a neural network (machine learning) requires differentiable functions and rules (used by expert systems) tend not to be differentiable. The method and system provide a data augmentation strategy where the expert system acts as an oracle and outputs the novel facts, which provide labels for the candidate triples. The novel facts provide critical information from the oracle that is injected into the machine learning model at the retraining stage, thus allowing to increase its generalization performance.

Abstract: A computer-implemented method and system for assigning at least one query triplet to at least one respective class. The at least one respective class is true or false. The method includes the steps of providing the at least one query triplet and a knowledge graph with a plurality of triples and extracting at least one affirmative argument using reinforcement learning on the basis of the at least one query triplet and the knowledge graph. The at least one affirmative argument indicates that the at least one query triplet is true. The method further includes extracting at least one opposing argument using reinforcement learning on the basis of the at least one query triplet and the knowledge graph. The at least one opposing argument indicates that the at least one query triplet is false. The method further includes assigning the at least one query triplet to the at least one respective class using supervised machine learning depending on the at least two arguments.

Abstract: A computer-implemented method for generating an adapted task graph, including the steps of providing a first input data set with at least one task graph and at least one task context and/or a second input data set with at least one constraint and at least one task context, generating an adapted task graph using a trained neural network based on the first input data set and/or the second input data set, and providing the adapted task graph.

Abstract: Provided is a method for computer-aided processing of quality information of an object manufactured by stacked printed layers in an additive manufacturing system, including the steps of: receiving a quality indicator for each printed layer of the object from the manufacturing system, assigning a color out of a predefined set of colors to each quality indicator depending on the value of the quality indicator, visualizing the quality indicators of the received manufactured layers as a sequence of colored bars ordered according to the sequence of the manufactured layers the color of each bar indicating the value of the quality indicator of the respective printed layer on a graphical user interface.

Abstract: Provided is a computer implemented method for providing an agent for creating a graph neural network architecture, which is suitable for providing a prediction of at least one indicator of a complex system and to a computer implemented method for providing such a graph neural network architecture by an agent. Also provide is an agent and a unit for providing an agent a computer program product and computer readable storage media.

Abstract: Provided is an assistance apparatus for localizing errors in a monitored technical system consisting of devices and/or transmission lines, including at least one processor configured to obtain values of actual attributes of the devices and/or of the transmission lines, determine an error probability for each device and/or transmission line by processing a graph neural network with the obtained actual values of attributes as input, wherein the graph neural network is trained by training attributes assigned to an attributed graph representation of the technical system, and output an indication for such devices and/or transmission lines, whose error probability is higher than a predefined threshold.

Abstract: A method including providing a graph database representation, wherein the graph database representation represents a plurality of nodes in a graph which are interconnected by respective edges, wherein each node of the plurality of the nodes represents a data sample and is assigned to at least one node feature, wherein each edge of the plurality of the edges represents a relationship between the data samples, transforming the graph database representation into a data matrix using a first machine learning algorithm suitable for graph data and an architecture as first layers of a joint machine learning architecture, determining at least one node of the plurality of nodes based on the transformed data matrix using a second machine learning algorithm and a second architecture as second layers of a joint machine learning architecture, and providing the at least one determined node. Further, a computing unit and a computer program product is provided.

Abstract: A computer implemented method for providing a service for a complex industrial system, the method including the steps of providing Bill of Materials, BoM, trees of system component instances of said complex industrial system; generating automatically a unified BoM data model by clustering matching nodes within the provided BoM trees; and performing the service for the complex industrial system based on the generated unified BoM data model is provided.

Abstract: Provided is a method for computer-aided processing of quality information of an object manufactured by stacked printed layers in an additive manufacturing system, including the steps of: receiving a quality indicator for each printed layer of the object from the manufacturing system, assigning a color out of a predefined set of colors to each quality indicator depending on the value of the quality indicator, visualizing the quality indicators of the received manufactured layers as a sequence of colored bars ordered according to the sequence of the manufactured layers the color of each bar indicating the value of the quality indicator of the respective printed layer on a graphical user interface.

Abstract: An apparatus and method for monitoring a quality of an object of a 3D-print job series of identical objects, each object built from a multitude of stacked 2D-layers printed by a 3D-printer in an additive manufacturing process, including: determining a layer quality indicator of a currently printed layer of an object, comparing the determined layer quality indicator of the currently printed layer with a predetermined lower confidence limit of the layer, the predetermined lower confidence limit being calculated depending on layer quality indicators of previously completely manufactured objects complying with predefined quality requirements, and generating a warning signal, if the layer quality indicator of the currently printed layer has a value equal or lower than the lower quality limit is provided.

Abstract: A method and system for automatic maintenance of a machine (2) comprising the steps of receiving (S1) at least one maintenance relevant event (E) from a controller (3) of the machine (2); augmenting (S2) the received event (E) with the event's machine context read from a machine maintenance ontology; matching (S3) the event's machine context with maintenance rules to generate at least one maintenance task (T) comprising an associated task description; and providing (S4) a maintenance schedule for the machine (2) assigning the generated maintenance task (T) to suitable maintenance executing entities (5) on the basis of the task description of the respective maintenance task (T).