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: Provided is neuromorphic hardware for storing and/or processing a knowledge graph with first neurons, representing a first node in the knowledge graph by first spike times of the first neurons during a recurring time interval, with second neurons, representing a second node in the knowledge graph by second spike times of the second neurons during the recurring time interval, and wherein a relation between the first node and the second node is represented as the differences between the first spike times and the second spike times.

Abstract: Provided is neuromorphic hardware for processing a knowledge graph, with a learning component, having an input layer containing node embedding populations of neurons, with each node embedding populations representing an entity contained in the observed statements, and an output layer, containing output neurons configured for representing a likelihood for each possible triple statement, and modeling a probabilistic, sampling-based model derived from an energy function, wherein the observed statements have minimal energy, and with a control component, configured for switching the learning component into a data-driven learning mode, configured for training the component with a maximum likelihood learning algorithm minimizing energy in the probabilistic, sampling-based model, using only the observed statements, which are assigned low energy values, in which the learning component supports generation of triple statements, and into a model-driven learning mode, configured for training the component, with the learni

Abstract: Provided is an industrial device for building and/or processing a knowledge graph, with at least one sensor and/or at least one data source configured for providing raw data, with an ETL component, configured for converting the raw data into triple statements, using mapping rules, with a triple store, storing the triple statements as a dynamically changing knowledge graph (with a learning component, configured for processing the triple statements in a learning mode, and for performing an inference in an inference mode, and with a control component, configured for switching between different modes of operation of the learning component.