Sap Se

Intelligent Views Gmbh

Sap Ag

Stefan Scheidl

Nico Licht

Steffen Moldaner

Stephan Brand

Klaus Reichenberger

Robert Heidasch

Archim Heimann

Michael Neumann

Matthias Kaiser

Christoph Meinel

Christian Lahmer

Todor Stoitsev

Thomas Pohl

Daniel Oberle

Uwe V Riss

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

The present disclosure describes methods, systems, and computer program products for importing data to a semantic graph. One computer-implemented method includes collecting, at a client, data from an information source; receiving, from a server, a query for information associated with the semantic graph; and sending, in response to the query, the collected data to the server in accordance to a communication protocol, wherein the collected data is imported to the semantic graph.

Importing data to a semantic 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

In a computer system, an ontology is derived from an original data source, such as from an relational database. At any time, a user can relate useful information to an ontology node, for example, by entering a document annotation into the ontology. Later, the user can retrieve the information through the annotation and view the document together with the ontology. An ontology update scheme keeps the document annotation available even if changes in the original data would remove the corresponding portions of the ontology. In the ontology, the scheme moves the annotations from a first ontology node to a second ontology node, by following a pre-defined change pattern.

Updating ontology while maintaining document annotations

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 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

In an embodiment, a method is provided for conducting a search. In this method, a message is received from a client application. A user that initiated the message is identified and context information associated with the user is retrieved. Thereafter, a business object associated with the context information is identified and a domain associated with the business object is identified. Here, the domain includes a number of terms, and one or more of these terms are selected to be included in a query. A search of a data source can then be conducted using this query.

Data search using context information

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

Embodiments include a system for providing an identification of a collaborative task. The system may include a communication unit and a processing unit. The communication unit may be configured to receive a request of a user to create the collaborative task and send task metadata to a further system. The processing unit may be configured to generate the task metadata. The task metadata may be a part of the collaborative task and identifying the collaborative task as a part of a process.

Method and system to process a request to create a collaborative task

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 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 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.

Semantic request normalizer

Various embodiments described herein provide systems, methods, software, and data structures that may be used in distributed task handling. Some embodiments include a generic architecture for loosely coupled associations of globally managed tasks and artifacts within user defined task descriptions. As a result, such embodiments provide a flexible and adaptable task model.

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Dieburg, Germany

Stefan Scheidl