Siemens Aktiengesellschaft

Rakebul Muff Hasan

Ulugbek Peter Kodirov

Dmitriy Fradkin

Martin Ringsquandl

Vladimir Lavrik

Alexander Storl

Matthias Loskyll

Mark Buckley

André Scholz

Johannes Maderspacher

Georgia Olympia Brikis

A method for integrating data from different data sources into a knowledge graph storage unit including:

Method and system for integrating data from different data sources into a knowledge graph storage unit

To restore consistency of a digital twin database, identifiers with metadata imported from various data sources are processed by an encoder, which computes latent representations of the identifiers that are compared by an efficient similarity metric. If the respective similarity score exceeds a threshold, a match is detected between the identifiers. In that case, the digital twin database is updated by aligning the first identifier and the second identifier. This matching algorithm for equipment identifiers updates the digital twin data automatically and continuously by aligning identifiers which refer to the same piece of equipment. The updates flow directly into the digital twin database, thereby removing the manual effort. Using approximate nearest neighbor methods is highly efficient, especially for large plants. The encoder is implemented as an autoencoder which relies only on unlabeled training data. This unsupervised approach is more suitable for industrial scenarios where labeled data is expensive to create.

Method and system for restoring consistency of a digital twin database

A computer-implemented method for determining a denoised named entity recognition (NER)-model and denoised relation extraction (RE)-model. A computer-implemented method for propagating an input dataset into a graph database representation, a computing unit and a computer program product, is also provided.

Determining a denoised named entity recognition model and a denoised relation extraction model

Provided is a computer-implemented method for receiving the at least two log files; wherein each log file of the at least two log files includes at least one log entry with at least one time stamp and at least one message; wherein the at least two log files differ from one another with respect to at least one distinctive criteria; extracting at least one additional information of each log file of the at least two log files; and combining each log file of the at least two log files with the extracted additional information into at least two processed log files; wherein the at least two processed log files comply with a coherent representation. A corresponding computer program product and generating unit is also provided.

Method for generating a coherent representation for at least two log files

Provided is a computer-implemented method for generating automatically annotations for tabular cell data of a table having column and rows, wherein the method includes: supplying raw cell data of cells of a row of the table as input to an embedding layer of a semantic type annotation neural network which transforms the received raw cell data of the cells of the supplied row into cell embedding vectors; processing the cell embedding vectors to calculate attentions among the cells of the respective row of the table encoding a context within the row output as cell context vectors; and processing the cell context vectors generated by the self-attention layer by a classification layer of the semantic type annotation neural network to predict semantic column type annotations and/or to predict relations between semantic column type annotations for the columns of the table.

Computer-implemented method and apparatus for automatically annotating columns of a table with semantic types

Provided is a method for performing entity linking between a surface entity mention in a surface text and entities of a knowledge graph, including supplying the surface text to a contextual text representation model, pooling contextual representations of the tokens of a surface entity mention in the surface text with contextual representations of the other tokens within the surface text to provide a contextual entity representation vector representing the surface entity mention; supplying an identifier of a candidate knowledge graph entity to a knowledge graph embedding model, to provide an entity node embedding vector and combining the contextual entity representation vector with the entity node embedding vector to generate an input vector applied to a fully connected layer which provides an unnormalized output transformed by a softmax function into a normalized output processed to classify whether the surface entity mention corresponds to the candidate knowledge graph entity.

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Munich, Germany

Rakebul Muff Hasan