Sap Se

Sap Ag

Michael Hladik

Jan Portisch

Sandra Bracholdt

Christoph Meinel

Roland Kolata

Stephan Schub

Benjamin Merkle

Bjoern Pantel

Ralf Handl

Dirk Endesfelder

Andreas Heinzmann

Frank Andreas Raebiger

Markus Roeckelein

Ekkehard Lange

Oliver Huth

Stefan Linkersdoerfer

Katharina Minninger

Robert Weller

Guilherme Ehrhardt Soares Ferreira Da Costa

Yannik Hahn

Thomas Maur

Stefan Schonger

Andreas S Borchardt

Christian Gschloessl

Manfred Preissendoerfer

Eric Berettoni

Martin Erdelmeier

In an example embodiment, a system is provided that takes a knowledge graph as input and generates multiple question templates based on predefined query types. These query types may be, for example, SPARQL query types and may also be configurable. The system repeatedly presents a single randomly or pseudo-randomly picked question template to a user, and asks the user to type a corresponding natural language question. The system persists the answer provided by the user with the question in a specialized format. Then, for each typed question, the system creates additional linguistic variations and asks users to rate them.

Question-answer reference data set generation for artificial intelligence

Techniques and solutions are provided for improved data modelling, including techniques that facilitate data model comparison, integration, or deployment. A core data model is created that has a plurality of elements, which can represent semantic concepts, including relationships between semantic concepts. A plurality of domain models are mapped to core data model elements. In particular, when a domain model element is to be created, the element is mapped to a core data model element. When a relation is to be established between two domain model elements, the relation is mapped to a relation type represented in the core data model. Mapping to a core data model can be enforced as part of domain model creation, allowing different domain models to be compared, since domain elements in different domain models that represent a common semantic will be mapped to the same core data model element.

Automatic enforcement of standardized data model mapping

An intelligent labeling method is disclosed herein. The method can receive a plurality of input instances and access a knowledge graph comprising a plurality of nodes and a plurality of edges connecting the plurality of nodes. The plurality of nodes represent objects and the edges represent relationship between the objects. For at least some selected input instances, the method can identify respective matching objects represented by corresponding nodes of the knowledge graph. A selected input instance shares a common label with the corresponding matching object. For identified matching objects, the method can identify a common class object represented by a corresponding node of the knowledge graph. A percentage of identified matching objects are descendants of the common class object, and the percentage is greater than a predefined threshold. The method can associate a label of the common class object with the plurality of input instances.

Systems and methods for intelligent labeling of instance data clusters based on knowledge graph

Techniques and solutions are described for converting data models between formats, such as between a conceptual data model and a physical data model for a database system, or between a conceptual data model and artefacts to be implemented in the database system. The conceptual data model is annotated with annotations that allow the physical data model or database artefacts to be automatically generated from the conceptual data model. The annotations can reflect relationships between entity types in the physical data model, such as inheritance relationships, header/item relationships, or one-to-one cardinality relationships. Annotations can also indicate attributes that should be added to entity types in the conceptual data model, such as attributes for versioning or data governance, that may not be used in the conceptual data model. Annotations can be used to determine how entity types in the conceptual data model will be denormalized in the physical data model.

Automatic conversion of data models using data model annotations

In a banking application system including numerous banking applications, a data management framework allows for the generalized storage and subsequent access of result data. Through the application of the framework to the raw processing data generated by one of the banking applications, the result data is stored in a general storage location. Through this framework, the data is then readily accessible by all other banking applications. This framework includes a header field, characteristics data and key figure data. This removes proprietary restrictions on the data itself and thus makes it available to the other applications, increasing cross-system efficiency by allowing other applications to access and subsequently use the result data.

Framework for integrated storage of banking application data

A method and system for bundling objects containing related data. Objects that are related to each other are bundled together to avoid synchronization problems. The bundling process is performed in parallel, as are operations, such as calculations, performed on the objects.

Bundling database

A system for organizing data is provided that includes a default template library and a universe of fields, each of the fields selectable by a user to apply to a particular modifiable template. The default template library includes a plurality of modifiable templates adapted to describe financial transactions. The system may further includes a processor adapted to relate business data according to a plurality of relations established by the particular modifiable template. The template relates the subset of fields to describe a financial transaction. A method is provided for structuring financial data in a database of transactions that includes selecting a template of a transaction and selecting at least one field for the financial data from a set of fields associated with the template to form a customized template. The customized template describes a relationship between the financial data.

Master data framework

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Walldorf, Germany

Michael Hladik