Abstract: Provided is a method for computer-implemented determination of a data-driven prediction model. The method processes digital input data having input and output variables and being semantically annotated based on a digital semantic representation having a hierarchical tree structure where each tree in the structure represents an input variable of the data, the leaf nodes of the respective tree being the discrete values of the input variable. The method of the embodiment provides a recoding of those discrete values by cutting off hierarchical levels of the respective trees. Based on this recoding, a plurality of data modifications is determined for the input data. Those data modifications are trained by a machine learning method where the trained machine learning method with the highest prediction quality is derived from the trained machine learning methods.

Abstract: Provided is a method for computer-implemented determination of a data-driven prediction model. The method processes digital input data having input and output variables and being semantically annotated based on a digital semantic representation having a hierarchical tree structure where each tree in the structure represents an input variable of the data, the leaf nodes of the respective tree being the discrete values of the input variable. The method of the embodiment provides a recoding of those discrete values by cutting off hierarchical levels of the respective trees. Based on this recoding, a plurality of data modifications is determined for the input data. Those data modifications are trained by a machine learning method where the trained machine learning method with the highest prediction quality is derived from the trained machine learning methods.

Abstract: Provided is a system for feedback-improved automatic solving of production facility related tasks, including: an input interface being adapted to receive production facility related data; a semantic data enhancement module being adapted to generate semantically enhanced data based on the production facility related data; a semantic-based reasoning module being adapted to automatically provide an explainable artificial intelligence model, using the semantically enhanced data, wherein the artificial intelligence model relates to a predetermined production facility related task; a user interaction interface being adapted to output explanation data regarding the explainable artificial intelligence model to a user; and a feedback module adapted to receive feedback from the user in response to the outputted explanation data and adapted to adjust the semantic-based reasoning module and/or the user interaction interface based on the received feedback.

Abstract: Provided is an apparatus and method for providing an instantiated industrial semantic model, iISM, for an industrial infrastructure having data generation components, the apparatus including: an interface unit adapted to receive from an industrial infrastructure data generated by data generation components provided within the industrial infrastructure, a context management unit adapted to derive types of data generation components provided within the industrial infrastructure depending on characteristics of the received data and adapted to map each derived type of data generation component to an associated context model specified in the industrial semantic model, ISM, stored in a repository, and an instantiating unit adapted to instantiate the stored industrial semantic model, ISM, with predefined industrial instance models, IIMs, of data generation components on the basis of the mapped context models to generate the instantiated industrial semantic model, iISM, of the industrial infrastructure.

Abstract: Production logs and industrial ontologies are processed with an inductive logic program performing class expression learning in order to create class expressions, with each class expression representing a constraint or property of a skill of a production module. The resulting class expressions are ordered by a metric to form an ordered recommender list and displayed to a user for postprocessing. The user selects suitable class expressions from the ordered recommender list, so that the system can build a machine-readable skill description with the selected class expressions. This approach to generating formal, machine-readable skill descriptions minimizes the labor time and domain expertise needed to equip production modules with their skill description. Selecting the correct class expression from the automatically generated ordered recommender list is a much lower effort than manual labeling from scratch.

Abstract: Provided is an apparatus and method for providing an instantiated industrial semantic model, iISM, for an industrial infrastructure having data generation components, the apparatus including: an interface unit adapted to receive from an industrial infrastructure data generated by data generation components provided within the industrial infrastructure, a context management unit adapted to derive types of data generation components provided within the industrial infrastructure depending on characteristics of the received data and adapted to map each derived type of data generation component to an associated context model specified in the industrial semantic model, ISM, stored in a repository, and an instantiating unit adapted to instantiate the stored industrial semantic model, ISM, with predefined industrial instance models, IIMs, of data generation components on the basis of the mapped context models to generate the instantiated industrial semantic model, iISM, of the industrial infrastructure.

Abstract: Provided is a method for computer-implemented determination of a data-driven prediction model. The method processes digital input data having input and output variables and being semantically annotated based on a digital semantic representation having a hierarchical tree structure where each tree in the structure represents an input variable of the data, the leaf nodes of the respective tree being the discrete values of the input variable. The method of the embodiment provides a recoding of those discrete values by cutting off hierarchical levels of the respective trees. Based on this recoding, a plurality of data modifications is determined for the input data. Those data modifications are trained by a machine learning method where the trained machine learning method with the highest prediction quality is derived from the trained machine learning methods.

Abstract: Provided is a system for the maintenance of a plant with at least one input unit for inputting semantically annotated observations of a user with respect to at least one system component of the plant; and a data processing unit for determining maintenance procedures for the maintenance of the plant on the basis of a semantic plant data model of the plant and on the basis of relationship data models, which indicate relationships between plant component states and observations, as a function of the semantically annotated observations inputted by means of the input unit.

Abstract: A data analyzing system for analyzing health-related data of a patient is provided. The system includes a data repository configured to store a semantic patient data model instantiated for each patient by episodic health-related data of the patient loaded from heterogeneous clinical data sources, by non-episodic health-related data of the patient loaded from at least one health monitoring device of the patient, or by a combination thereof. The system also includes a data analyzing unit configured to analyze at least one instantiated patient data model stored in the data repository in response to a query.

Abstract: A method for generating a knowledge data model is provided. The method includes providing at least one initial set of semantic type entities of a specific semantic type. The initial set of semantic type entities is expanded using available mappings between entities of the initial set and entities of unspecified type to generate an extended set of semantic type entities. Entities of a same semantic type are clustered within the extended set of semantic type entities. The method maps semantic relations between entities of different semantic type to relations between corresponding clusters containing the entities to generate the knowledge data model.

Abstract: A system for providing data analytics results for a process performed in an industrial plant includes a knowledge model repository configured to store semantic knowledge models. The system also includes selection unit configured to select at least one analytics application describing semantically at least one process step and at least one parameter required for accomplishing an analytics task. The system includes a processing unit configured to process the selected analytics application and selected instantiated semantic knowledge models to infer at least one storage location of at least one data source of the industrial plant, and an execution engine configured to execute the selected data analytics application using accessed process data provided by the inferred data sources of the industrial plant to generate the data analytics results.

Abstract: A system for extracting relations between measurements within an unstructured text and ontology concepts of at least one domain ontology stored in a domain ontology database is provided. The system includes an annotation unit adapted to process sentences of the unstructured text to derive tokens and measurements within the sentences. The derived tokens are annotated with ontology concepts mapped to the tokens. The system also includes a concept analyzing unit adapted to analyze, for each annotated sentence including at least one derived measurement, the annotated ontology concepts mapped to the derived tokens of the sentence to identify the ontology concepts related to the at least one derived measurement and to rank the identified related ontology concepts according to calculated relation strengths of the relations between the identified related ontology concepts and the respective measurement of the annotated sentence.

Abstract: Medical experts are supported in the process of specifying and fine-tuning initial search requests by aggregating additional information about a patient context (e.g., patient, assumption, internal diagnose, external diagnose and procedure context). Mismatching information units are subsequently used as an entry point for improved and tailored information access by question answering systems. Different to traditional similarity-driven evidence ranking, an approach that does not disregard the mismatching information but emphasizes such silent signals is established.

Abstract: A method and system for supporting a clinical diagnosis is provided, the method including the steps of representing a patient by a plurality of initial symptoms, the symptoms including characteristics of the patient; determining, by querying and reasoning in a disease-symptom knowledge model, at least one likely disease for each of the plurality of initial symptoms; mapping the plurality of initial symptoms onto a set of said likely diseases; determining for each disease of said set of likely diseases a plurality of associated symptoms, the associated symptoms being associated to a respective disease of said set of likely diseases in said disease-symptom knowledge model; categorizing each of the plurality of associated symptoms into disjoint categories by mapping associated symptoms with initial symptoms; and representing likely diseases in the context of said categories of associated symptoms.

Abstract: Concept labels of an ontology are extended. An ontology includes concepts at least partially authored in a source language; a corpus at least partially authored in a target language is provided; said corpus is processed by a linguistical analysis and receiving a list of first terms as a result of said linguistical analysis, said first terms in said list ordered by a linguistical relevancy; within said list, at least one of said first terms is associated with an associated second term, said second term being a translation of said first term into said source language; a retrieval is conducted by using at least one of said second terms for identifying a matching concept within said ontology; and a concept label of said matching concept is extended by a first term associated to said second term of a matching retrieval.

Abstract: A method of composing an integrated ontology is provided. The method makes use of feature models for the process of modular development of ontologies. The feature models are adapted to assist the user in the process of selection of appropriate and combinable ontologies from an ontology repository for a given use case scenario. The usage of a feature model provides a view of all possible combinations, i.e. all combinations that do not result in an inconsistent ontology. The feature model is specifying dependencies between different ontologies thereby expediting the selection of appropriate ontologies. The usage of reasoning techniques provides for an in-situ consideration of restrictions for the selection of ontologies.

Abstract: Methods and systems may support conclusions of a clinician by enabling the clinician to arbitrarily define the symptoms according to the patient's record. The method may comprise: (a) determining a plurality of symptoms representing a patient and including characteristics of the patient, the symptoms being categorized as present symptoms, absent symptoms or open symptoms; (b) establishing a multi-tier relationship between said symptoms and a plurality of diseases based on a knowledge model; (c) adjusting weighting factor(s) for at least one multi-tier relationship based on said knowledge model; (d) optionally adjusting at least one weighting factor of step (c) by an expert; and (e) building a ranking of diseases from said multi-tier relationship based on said weighting factors. The method may provide an efficient ranking of disease information, thereby supporting a clinical diagnosis that may consider multiple symptoms while emphasizing particular symptoms in view of the clinician's expertise.

Abstract: A method for processing medical reports includes applying natural language processing methods to perform an initial segmentation of at least parts of textual contents of each medical report into information units, thereby identifying information units, such as text fragments, sentences or text passages, within the medical reports. The information units are classified into at least one context class to determine their appropriate context classes for a particular situation or application. The context classifications are created using a grammar, e.g., a context-free grammar, and can then be used for automatically assigning each information unit to an appropriate context meta-information. The medical report may be annotated by assigning the information units to context meta-information determined by the context class, and the context meta-information may be used by other applications, e.g.

Abstract: A method and system for providing instance information data of at least one instance. The method has the steps of generating a semantic representation of the instance on the basis of at least one instance data source, providing a semantic representation of a domain on the basis of at least one structured domain data source, performing an integration of the semantic instance and domain representations to provide an integrated semantic representation and deriving automatically instance information data from the provided integrated semantic representation. The method and system allow to provide efficiently instance information data of a wide range of instances with technical entities and persons, in a particular patients in different domains such as radiology or anatomy.

Abstract: Methods for modeling a physical facility and corresponding systems and computer-readable mediums. A method includes receiving a plant concept model of a physical facility including a plurality of metamodel entities and receiving a plurality of plant foundational models corresponding to the plant concept model, each plant foundational model addressing a different engineering aspect of the physical facility. The method includes integrating the plurality of plant foundational models and defining a plurality of plant type models each corresponding to a respective plant foundational model. The method includes defining a plurality of plant instance models each corresponding to a respective plant type model and creating an integrated model that provides a user view that combines the plant foundational models and plant instance models. The method includes storing the integrated model.