Domain Knowledge Driven Semantic Extraction System

• US 20150363386A1
• Filed: 06/17/2014
• Published: 12/17/2015
• Est. Priority Date: 06/17/2014
• Status: Active Grant

• Current Assignee: Business Objects Incorporated (SAP SE)
• Original Assignee: Business Objects Incorporated (SAP SE)
• Inventors: Song, Yuqian
• Granted Patent: US 9,582,495 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/36 Creation of semantic tools, e.g. ontology or thesauri
• CPC Class Codes: G06F 16/367 Ontology
• CPC Class Codes: G06F 40/30 Semantic analysis

Abstract

A semantic extraction system leverages domain expert knowledge, to impart meaningful business information aiding ordinary knowledge consumers in understanding large/complex data volumes and models thereof. Certain embodiments may comprise a layered structure comprising an information uplifting layer, a semantic processing layer, and a visual representation layer. Referencing domain knowledge model(s) created by human domain experts, the information uplifting layer extracts and maintains meaningful information in a semantic structure. The semantic processing layer then processes this extracted information for various different business analysis purposes. Finally, the visual representation layer allows the analyzed and aggregated information to be arranged and visualized via a range of interactive tools. The overall layered structure is powered by the domain knowledge models, which capture specialized knowledge from experts in different domains. Such domains can include industry and enterprise characteristics, data visualization, and model structure and function.

Claims

1. A computer-implemented method comprising:

providing an engine in communication with a database configured to store a data type;

causing the engine to receive a heterogeneous input comprising an element;

causing the engine to reference a schema of a knowledge model created by a domain expert, to produce a mapping of the element with a class of the mapping schema; and

causing the engine to output a resource model comprising the mapping and a relationship between the element and the data type.

2. A method as in claim 1 wherein the resource model further comprises a written description of the element.

3. A method as in claim 1 wherein the schema identifies a format of the data type.

4. A method as in claim 1 further comprising causing the engine to consume the resource model to produce a semantic attribute annotation of the element.

5. A method as in claim 4 further comprising causing the engine to execute a pattern detection algorithm to produce the semantic attribute annotation.

6. A method as in claim 4 further comprising causing the engine to perform decision support referencing a semantic entity annotation derived from the semantic attribute annotation.

7. A method as in claim 1 wherein the class is encoded in web ontology language (OWL).

8. A non-transitory computer readable storage medium embodying a computer program for performing a method, said method comprising:

providing an engine in communication with a database configured to store a data type;

causing the engine to receive a heterogeneous input comprising an element;

causing the engine to reference a schema of a knowledge model created by a domain expert, to produce a mapping of the element with a class of the mapping schema; and

causing the engine to output a resource model comprising the mapping and a relationship between the element and the data type.

9. A non-transitory computer readable storage medium as in claim 8 wherein the resource model further comprises a written description of the element.

10. A non-transitory computer readable storage medium as in claim 8 wherein the schema identifies a format of the data type.

11. A non-transitory computer readable storage medium as in claim 8 further comprising causing the engine to consume the resource model to produce a semantic attribute annotation of the element.

12. A non-transitory computer readable storage medium as in claim 11 further comprising causing the engine to execute a pattern detection algorithm to produce the semantic attribute annotation.

13. A non-transitory computer readable storage medium as in claim 11 further comprising causing the engine to perform decision support referencing a semantic entity annotation derived from the semantic attribute annotation.

14. A non-transitory computer readable storage medium as in claim 8 wherein the class is encoded in web ontology language (OWL).

15. A computer system comprising:

one or more processors;

a software program, executable on said computer system, the software program configured to;

provide an engine in communication with a database configured to store a data type;

cause the engine to receive a heterogeneous input comprising an element;

cause the engine to reference a schema of a knowledge model created by a domain expert, to produce a mapping of the element with a class of the mapping schema; and

cause the engine to output a resource model comprising the mapping and a relationship between the element and the data type.

16. A computer system as in claim 15 wherein the resource model further comprises a written description of the element.

17. A computer system as in claim 15 wherein the schema identifies a format of the data type.

18. A computer system as in claim 15 wherein the software program is further configured to cause the engine to consume the resource model to produce a semantic attribute annotation of the element.

19. A computer system as in claim 18 wherein the software program is further configured to cause the engine to execute a pattern detection algorithm to produce the semantic attribute annotation.

20. A computer system as in claim 18 wherein the software program is further configured to cause the engine to perform decision support referencing a semantic entity annotation derived from the semantic attribute annotation.