Semantic related objects

Patent metadata

Field	Value
Publication number	US-9779135-B2
Application number	US-201113288730-A
Country	US
Kind code	B2
Filing date	Nov 3, 2011
Priority date	Nov 3, 2011
Publication date	Oct 3, 2017
Grant date	Oct 3, 2017

Abstract

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.

First claim

What is claimed is:

1. A method, utilizing at least one computing device, comprising:
retrieving data definitions defining types of a plurality of business objects stored in enterprise data, the data definitions specify one or more attributes for each of the types of the plurality of business objects;
generating, based at least in part on the data definitions, a meta-model of the enterprise data, the meta-model provides semantic information characterizing conceptual meaning to the one or more attributes;
using the meta-model of the enterprise data to generate a rule definition that maps the enterprise data to the semantic information;
using the rule definition to generate at least one semantic object and at least one semantic relation from the plurality of business objects stored in the enterprise data; and
storing the at least one semantic object and the at least one semantic relation in a meta-model semantic network, the meta-model semantic network associating a term to the at least one semantic object.

2. The method of claim 1 , wherein the generating of the meta-model of the enterprise data includes retrieving preexisting data definitions associated with the enterprise data.

3. The method of claim 1 , wherein the meta-model semantic network is optimized for a specific type of query.

4. The method of claim 1 , further comprising:
receiving a message with a search query;
identifying a relevant term in the search query;
identifying that the at least one semantic relation is associated with the search query;
searching the meta-model semantic network for semantic objects linked to the relevant term according to the at least one semantic relation; and
communicating the semantic objects in a search result.

5. The method of claim 4 , further comprising obtaining the meta-model semantic network based on the search query, wherein the meta-model semantic network is optimized for the search query.

6. The method of claim 1 , wherein the enterprise data further includes documents and user feedback objects.

7. The method of claim 1 , wherein the at least one semantic relation is associated with a weighted value.

8. The method of claim 1 , wherein the meta-model provides semantic information regarding the one or more attributes by defining conceptual types representing valid attribute types for the one or more attributes specified by the data definitions.

9. The method of claim 8 , further comprising, in response to a user input, mapping one of the conceptual types to the one or more attributes specified by the data definitions.

10. A non-transitory, machine-readable medium that stores instructions, which, when performed by a machine, cause the machine to perform operations comprising:
retrieving data definitions defining types of a plurality of business objects stored in enterprise data, the data definitions specify one or more attributes for each of the types of the plurality of business objects;
generating, based at least in part on the data definitions, a meta-model of the enterprise data, the meta-model provides semantic information characterizing conceptual meaning to the one or more attributes;
using the meta-model of the enterprise data to generate a rule definition that maps the enterprise data to the semantic information;
using the rule definition to generate at least one semantic object and at least one semantic relation from the plurality of business objects stored in the enterprise data; and
storing the at least one semantic object and the at least one semantic relation in a meta-model semantic network, the meta-model semantic network associating a term to the at least one semantic object.

11. The non-transitory, machine-readable medium of claim 10 , wherein the generating of the meta-model of the enterprise data includes retrieving existing data definitions associated with the enterprise data.

12. The non-transitory, machine-readable medium of claim 10 , wherein the meta-model semantic network is optimized for a specific type of query.

13. The non-transitory, machine-readable medium of claim 10 , further comprising:
receiving a message with a search query;
identifying a relevant term in the search query;
identifying that the at least one semantic relation is associated with the search query;
searching the meta-model semantic network for semantic objects linked to the relevant term according to the at least one semantic relation; and
communicating the semantic objects in a search result.

14. The non-transitory, machine-readable medium of claim 13 , further comprising obtaining the meta-model semantic network based on the search query, wherein the meta-model semantic network is optimized for the search query.

15. The non-transitory, machine-readable medium of claim 10 , wherein the enterprise data further includes documents and user feedback objects.

16. The non-transitory, machine-readable medium of claim 10 , wherein the at least one semantic relation is associated with a weighted value.

17. An apparatus comprising:
at least one processor implemented at least partially by hardware; and
a meta-model semantic network manager configured by the at least one processor to:
retrieve data definitions defining types of a plurality of business objects stored in enterprise data, the data definitions specify one or more attributes for each of the types of the plurality of business objects;
generate, based at least in part on the data definitions, a meta-model of the enterprise data, the meta-model provides semantic information characterizing conceptual meaning to the one or more attributes;
use the meta-model of the enterprise data to generate a rule definition that maps the enterprise data to the semantic information;
use the rule definition to generate at least one semantic object and at least one semantic relation from the plurality of business objects stored in the enterprise data; and
store the at least one semantic object and the at least one semantic relation in a meta-model semantic network, the meta-model semantic network associating a term to the at least one semantic object.

18. The apparatus of claim 17 , wherein the generating of the meta-model of the enterprise data includes retrieving existing data definitions associated with the enterprise data.

19. The apparatus of claim 17 , wherein the meta-model semantic network is optimized for a specific type of query.

20. The apparatus of claim 17 , wherein the meta-model semantic network manager is further configured to:
receive a message with a search query;
identify a relevant term in the search query;
identify that the at least one semantic relation is associated with the search query;
search the meta-model semantic network for semantic objects linked to the relevant term according to the at least one semantic relation; and
communicate the semantic objects in a search result.

21. The apparatus of claim 17 , wherein the enterprise data further includes documents and user feedback objects.

22. The apparatus of claim 17 , wherein the at least one semantic relation is associated with a weighted value.

Assignees

• Heidasch Robert
• Scheidl Stefan
• Neumann Michael
• Kaiser Matthias
• Brand Stephan
• Licht Nico
• Reichenberger Klaus
• Heimann Archim
• Meinel Christoph
• Moldaner Steffen
• Sap Se
• Intelligent Views Gmbh

Inventors

• Heidasch Robert
• Scheidl Stefan
• Neumann Michael
• Kaiser Matthias
• Brand Stephan
• Licht Nico
• Reichenberger Klaus
• Heimann Archim
• Meinel Christoph
• Moldaner Steffen

Classifications

• G06F16/907

  Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually · CPC title

• G06F16/24522 Primary

  Translation of natural language queries to structured queries · CPC title

• G06F16/36

  Creation of semantic tools, e.g. ontology or thesauri · CPC title

• G06F16/3334

  Selection or weighting of terms from queries, including natural language queries · CPC title

• G06F16/3326

  using relevance feedback from the user, e.g. relevance feedback on documents, documents sets, document terms or passages · CPC title