Siemens Aktiengesellschaft

Siemens Energy Global Gmbh & Co. Kg

Bernt Andrassy

Pankaj Gupta

Subburam Rajaram

Thomas Hubauer

Mikhail Roshchin

Thomas A Runkler

Denis Krompaß

Felix Buggenthin

Giuseppe Fabio Ceschini

Sigurd Spieckermann

Mark Buckley

Davood Naderi

Richard Arnatt

Michael Werner

Zlatan Cota

Almir Avdovic

Provided is a ticketing system adapted to retrieve a recommendation from a knowledge database in response to a received query, the ticketing system including a processor adapted to perform semantic similarity learning in textual description pairs by calculating similarity scores for similarities between the received query and tickets stored in the knowledge database of the ticketing system, wherein each textual description pair includes a textual description of the received query and a textual description of a ticket of a plurality of tickets stored in the knowledge database of the ticketing system, wherein the ticket having the maximum similarity score is identified and a solution of the identified ticket is output as the retrieved recommendation for the received query by the ticketing system.

Method for retrieving a recommendation from a knowledge database of a ticketing system

A method and an apparatus for optimizing diagnostics of rotating equipment is provided. The apparatus includes a device for providing status information about status of the rotating equipment over a series of time windows whereby status can be derived from sensor features of at least one available sensor taking measurements during a predefinable time period, a device for using deep learning which combines provided historic sensor information with sequence of events data indicating warnings and/or alerts of the rotating equipment, whereby status information is supplemented with via deep learning predicted probabilities whether a warning and/or an alert has occurred within a time window, device for providing an amount of textual diagnostic knowledge cases, device for extracting semantic information on text features from the textual diagnostic knowledge cases, and device for combining status information and semantic information into a unified representation enabling optimization of the diagnostics.

Method and apparatus for optimizing diagnostics of rotating equipment

A method and system for automatically performing a discovery of topics within temporal ordered text document collections are provided. The method includes generating a bag of words vector for each text document collection using a predefined dictionary. The method also includes iteratively calculating, based on the generated bag of words vectors, for each text document collection, a hidden topic vector representing topics of the respective text document collection using a calculated hidden state vector memorizing a hidden state of all previous text document collections.

Method and system for automatic discovery of topics and trends over time

Provided is a computer-implemented method for inter-sententially determining a semantic relationship between a first entity and a second entity in a natural language document, comprising at least the steps of:

Neural relation extraction within and across sentence boundaries

A Semantic Textual Similarity System comprising a first Long Short Term Memory, LSTM, branch adapted to be operative, to determine text similarity, on a first text corpus, the first text corpus comprising a plurality of first text elements; wherein each first text element has a first number of distinct subdivisions. The system also comprises a second LSTM branch adapted to be operative, to determine text similarity, on a second text corpus, the second text corpus comprising a plurality of second text elements, wherein each second text element has a second number of distinct subdivisions.

Semantic textual similarity system

A device for relation extraction in a natural language sentence having n words is suggested, the device comprising: a recurrent neural network for joint entity and relation extractions of entities and relations between the entities in the sentence, and an entity-relation table for storing entity labels for the entities and relation labels for the relations, wherein both the entity labels and the relation labels are defined as instances of binary relationships between certain words wand win the sentence, with i&#x2208;[1, . . . , n], and j&#x2208;[1, . . . , n], wherein each of the entity labels is a first binary relationship for i=j, and wherein each of the relation labels is a second binary relationship for i&#x2260;j, wherein the recurrent neural network is configured to fill the cells of the entity-relation table and includes a forward neural network and a backward neural network.

Device and method for natural language processing

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Munich, Germany

Bernt Andrassy