Sap Se

Sap Ag

Intelligent Views Gmbh

Robert Heidasch

Stefan Scheidl

Nico Licht

Stephan Brand

Klaus Reichenberger

Steffen Moldaner

Michael Neumann

Archim Heimann

Matthias Kaiser

Albert Rossmann

Christoph Meinel

Thomas Pohl

Christian Lahmer

A method and apparatus for determining relationships between objects in a meta-model semantic network is described. A contextual network graph comprising nodes and edges representing semantic objects and semantic relationships is generated from a meta-model of business objects from the meta-model semantic network. The contextual network graph is used to generate a unique identifier for each node and associated edge. The unique identifiers are used to compute information of cost and energy between the nodes. The information is stored in a meta-model semantic network database.

Distance in contextual network graph

In an embodiment, a method is provided for searching similar documents. Here, a document is accessed and terms from a metamodel semantic network is identified. The document is analyzed to identify a number of the terms from the metamodel semantic network that are also found in the document, and to identify a frequency of occurrence in the document for each term. A search is conducted for other documents having frequencies of occurrences that are similar to the identified frequency of occurrence. These other documents have been previously analyzed using the same terms from the metamodel semantic network.

Searches for similar documents

In an embodiment, a method is provided for utilizing a meta-model semantic network. In this method, a meta-model of the enterprise data is obtained. The meta-model provides semantic information regarding a definition of a business object. The meta-model is then used to generate a rule definition that maps enterprise data to a semantic object definition and a semantic relation definition. With the rule definition, embodiments may then generate a semantic object and a semantic relation from data extracted from the enterprise data. The semantic object and semantic relation are stored in the meta-model semantic network.

Semantic related objects

A semantic phrase suggestion engine that provides term and sentence suggestions based on context-specific user groups. Knowledge domains within a semantic network may be automatically derived from user software applications, and each term within the knowledge domain includes meta-data about the terms, e.g., term type and an importance indicator. The indicators may be defined within the context of specific user groups and relate to how many times that group has used the term (e.g., in documents, emails, etc.) The semantic phrase suggestion engine may also include spelling conditions and grammar conditions, which can then provide phrase suggestions according to the conditions and importance indicators, specific to a user group.

Semantic phrase suggestion engine

In an embodiment, a method is provided for customizing a task associated with a business process. In this method, a business process platform is accessed. The business process platform includes a business process definition and a provider semantic configuration. A group semantic configuration is generated, where the group semantic configuration extends the provider semantic configuration. A task defined in the business process definition is the customized based on the group semantic configuration.

Semantic configuration in a business process platform

In example embodiments, a technique is provided to determine the similarity between two terms. For example, example embodiments may store a meta-model semantic network that includes a first and second term. Further, both the first and second terms are respectively associated with model and meta-model information. A request to calculate a term similarity value is received. A term similarity value expresses a correlation between the first term and the second term. The term similarity value is then calculated based on a comparison of the model and the meta-model information associated with the first and second terms.

Calculating term similarity using a meta-model semantic network

A method, machine readable storage medium, and system for providing a self learning semantic search engine. A semantic network may be set up with initial configuration. A search engine coupled to the semantic network may build indexes and semantic indexes. A user request for business data may be received. The search engine may be accessed via a semantic dispatcher. And based on the access, search engine may update the indexes and semantic indexes.

Self-learning semantic search engine

A method, machine readable storage medium, and system for providing personalized semantic controls for semantic systems. A semantic network may be set up with initial configuration. A business application user interface, including semantic controls, may be coupled to the semantic network to interact with the semantic network. Semantic objects and relations may be defined in the semantic network for business terminology. A user request for business data may be received. The semantic network may update the objects and relations for business terminology based on the request. The business application user interface may provide for personalized semantic controls based on the updated objects and relations.

Personalized controls for a semantic system utilizing a central and a local semantic network

A ranking in cascading learning system is described. The cascading learning system has a request analyzer, a request dispatcher and classifier, a search module, a terminology manager, and a cluster manager. The request analyzer receives a request for search terms from a client application and determines term context in the request to normalize request data from the term context. The normalized request data are classified and dispatched to a corresponding domain-specific module with a request dispatcher ranking calibrator. Each domain-specific module of a search module generates a prediction with a trained probability of an expected output using a corresponding domain-specific ranking calibrator. The terminology manager receives normalized request data from the request dispatcher and classifier, and manages terminology stored in a contextual network. The cluster manager comprises a central ranking calibrator, a training and sot container, and a module generator configured to generate a pluggable module.

Ranking in cascading learning system

A method and apparatus for optimizing access to a contextual network are described. The apparatus has a registration manager module, a configuration manager module, an access manager module, and an access optimizer module. The registration manager module registers business applications operating in the contextual network of a server with registration data having a set of parameters to define the business applications. The configuration manager module determines initial configuration settings for the business applications to identify parts of the contextual network relevant to the business applications. The access manager module provides the business applications with access to contextual network data based on distances between nodes in a contextual network graph of the contextual network data. The access optimizer module collects measurements of the access of the business applications to the contextual network data, and computes future optimal configuration settings for the business applications.

Contextual network access optimizer

A cascading learning system for semantic search is described including the generation, training and testing of a domain-specific module for a domain-specific search. One or more input elements and output elements are specified for the domain-specific module with reference to a domain that relates these elements together through data sets that include related metadata. The related metadata may include semantic terms that are incorporated into a contextual network applicable to the domain.

Pluggable modules in a cascading learning system

A method and apparatus for detection of relationships between objects in a meta-model semantic network is described. Semantic objects and semantic relations of a meta-model of business objects are generated from a meta-model semantic network. The semantic relations are based on connections between the semantic objects. A neural network is formed based on usage of the semantic objects and the semantic relations. The neural network is integrated with the semantic objects and the semantic relations to generate a contextual network. A statistical analysis of the connections between the semantic objects in the contextual network is performed to identify stronger semantic relations. The identified stronger semantic relations are used to update the neural network. The updated neural network is integrated into the contextual network.

Learnable contextual network

A method and system that modularizes a message by separating the message definition data from the message data. The message definition data and message data are transmitted over a secure channel to a target computing device. The message definition data and message data are recombined to form the original message at the target computer using a process corresponding to the modularization process. A key is used to track the associated definitions and message data and determine the corresponding combination process. Separate transmission of the data definitions and message data provides an added level of security. If message data is intercepted and decrypted by a third party, then the data is not easily utilized, because the definition data is absent. Similarly, interception of the message definition is not useful without the message data.

Modular secure data transfer

According to some embodiments, mapping definition information may be transmitted from a business process engine to a business process client. Subsequent to said transmitting, it may be arranged for the business process client to receive business data formatted in accordance with the mapping definition information. The business data may, for example, include a series of values, and the mapping definition information may be used by the business process client to map the series of values to a plurality of data fields.

Systems and methods providing mapping definition information for business data

A cascading learning system as a normalized semantic search is described. The cascading learning system has a request analyzer, a request dispatcher and classifier, a search module, a terminology manager, and a cluster manager. The request analyzer receives a request for search terms from a client application. The request analyzer has a normalization manager, a semantic parser, and a context builder. The normalization manager normalizes the search terms and generates a normalized semantic request based on a context. The request dispatcher and classifier classifies and dispatches the normalized semantic request to a corresponding domain-specific module that generates a prediction with a trained probability of an expected output. The terminology manager receives the normalized semantic request from the request dispatcher and classifier, and manages terminology stored in a contextual network.

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Speyer, Germany

Robert Heidasch