COARSE SEMANTIC DATA SET ENHANCEMENT FOR A REASONING TASK

• US 20150154178A1
• Filed: 04/19/2013
• Published: 06/04/2015
• Est. Priority Date: 04/19/2013
• Status: Abandoned Application

• Current Assignee: Streamline Licensing LLC (IP Edge LLC)
• Original Assignee: Empire Technology Development LLC (Allied Inventors Management, LLC)
• Inventors: Fang, Jun
• Application Number:
  US14/412,412
• Publication Number:
  US 20150154178A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/215 Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06N 5/04 Inference or reasoning models

Abstract

Technologies are generally described for enhancing semantic data to be used by a reasoning task. In some examples, a method and a system for removing inconsistent data from, and adding enhancement data to, a coarse data set are described. The method may include receiving, by a data enhancement module, a first set of semantic data associated with the reasoning task. The method may include generating, by the data enhancement module, a second set of semantic data by removing inconsistent data from the first set of semantic data, wherein the inconsistent data is identified from the first set of semantic data by a justification determination process. The method may further include generating, by the data enhancement module, a third set of semantic data by adding enhancement data to the second set of semantic data, wherein the enhancement data is obtained based on the second set of semantic data by an abduction determination process.

Claims

1. A method for enhancing data to be used by a reasoning task, the method comprising:

receiving, by a data enhancement module, a first set of semantic data associated with the reasoning task;

generating, by the data enhancement module, a second set of semantic data by removing inconsistent data from the first set of semantic data, wherein the inconsistent data is identified from the first set of semantic data by a justification determination process; and

generating, by the data enhancement module, a third set of semantic data by adding enhancement data to the second set of semantic data, wherein the enhancement data is obtained based on the second set of semantic data by an abduction determination process.

2. The method of claim 1, further comprising:

generating a set of reasoning results by performing the reasoning task based on the third set of semantic data.

3. The method of claim 1, wherein the first set of semantic data contains an inconsistent and incomplete ontology for the reasoning task, and the third set of semantic data contains a consistent and complete ontology for the reasoning task.

4. The method of claim 1, wherein the justification determination process comprises:

identifying one or more justifications based on the first set of semantic data, wherein each of the one or more justifications contains a plurality of elements selected from the first set of semantic data, the plurality of elements are inconsistent in an ontology, and removing one element from the plurality of elements makes the rest of the plurality of elements consistent in the ontology;

identifying an inconsistent candidate based on the one or more justifications; and

appointing one or more elements in the inconsistent candidate as the inconsistent data removed from the first set of semantic data.

5. The method of claim 4, wherein identifying the inconsistent candidate comprises:

generating one or more relevance candidates from the one or more justifications;

for each relevance candidate in the one or more relevance candidates, calculating a corresponding semantic relatedness score based on the relevance candidate and the reasoning task; and

selecting the inconsistent candidate from the one or more relevance candidates for having a corresponding semantic relatedness score that is below a predetermined threshold.

6. The method of claim 5, wherein calculating the corresponding semantic relatedness score comprises:

selecting a first axiom from the inconsistent candidate and a second axiom from the reasoning task;

receiving, from a search engine, a first hit score for the first axiom, a second hit score for the second axiom, and a third hit score for a combination of the first axiom and the second axiom; and

calculating the corresponding semantic relatedness score by using the first hit score, the second hit score, and the third hit score.

7. The method of claim 5, wherein calculating the corresponding semantic relatedness score comprises:

selecting a first axiom from the inconsistent candidate and a second axiom from the reasoning task;

receiving, from a search engine, a first plurality of contents related to the first axiom and a second plurality of contents related to the second axiom; and

calculating the corresponding semantic relatedness score by using the first plurality of contents and the second plurality of contents.

8. The method of claim 1, wherein the abduction determination process comprises:

generating a plurality of abduction candidates based on an observation and the second set of semantic data;

for each abduction candidate selected from the plurality of abduction candidates, calculating a corresponding semantic relatedness score based on the abduction candidate and the observation;

selecting one or more enhancement candidates from the plurality of abduction candidates for having corresponding semantic relatedness scores that are above a predetermined threshold; and

adding the one or more enhancement candidates as the enhancement data to the second set of semantic data.

9. A method for enhancing data to be used by a reasoning task, the method comprising:

receiving, by a data enhancement module, a first set of data associated with the reasoning task;

identifying, by the data enhancement module via a justification determination process, inconsistent data from the first set of data;

generating, by the data enhancement module, a second set of data by removing the inconsistent data from the first set of data;

generating, by the data enhancement module via an abduction determination process, enhancement data based on the second set of data; and

generating, by the data enhancement module, a third set of data by adding the enhancement data to the second set of data, wherein the third set of data contains a self-consistent and self-complete ontology for the reasoning task.

10. The method of claim 9, wherein identifying the inconsistent data comprises:

calculating a plurality of justifications based on the first set of data, wherein each of the plurality of justifications contains a corresponding plurality of elements selected from the first set of data, and the corresponding plurality of elements are inconsistent in an ontology;

generating a plurality of relevance candidates based on the plurality of justifications; and

identifying an inconsistent candidate from the plurality of relevance candidates as the inconsistent data.

11. The method of claim 10, wherein calculating the plurality of justifications comprises:

dividing the first set of data into a first half of data and a second half of data;

upon a determination that the first half of data is inconsistent in the ontology, generating one of the plurality of justifications based on the first half of data.

12. The method of claim 11, wherein calculating the plurality of justifications further comprises:

upon a determination that the first half of data and the second half of data are inconsistent in the ontology, generating one of the plurality of justifications based on the first half of data and the second half of data.

13. The method of claim 10, wherein generating the plurality of relevance candidates comprises:

utilizing a Cartesian product of the plurality of justifications as the plurality of relevance candidates.

14. The method of claim 10, wherein identifying the inconsistent candidate comprises:

selecting one of the plurality of relevance candidates that has the least relatedness with the reasoning task as the inconsistent candidate.

15. The method of claim 9, wherein generating the enhancement data from the second set of data comprises:

obtaining a plurality of abduction candidates related to an observation based on the second set of semantic data; and

selecting a plurality of enhancement candidates from the plurality of abduction candidates as the enhancement data for having corresponding semantic relatedness scores that are above a predetermined threshold.

16. A system for performing a reasoning task, the system comprising:

a data enhancement module configured to

receive a first set of semantic data,

generate a second set of semantic data by removing inconsistent data from a first set of semantic data, the inconsistent data being identified from the first set of semantic data by a justification determination process, and

generate a third set of semantic data by adding enhancement data to the second set of semantic data, the enhancement data being obtained based on the second set of semantic data by an abduction determination process; and

a reasoning engine coupled with the data enhancement module, the reasoning engine configured to generate a set of reasoning results based on the third set of semantic data.

17. The system as recited in claim 16, wherein the data enhancement module comprising:

an inconsistency reduction unit configured to identify the inconsistent data; and

a completeness enhancement unit configured to obtaining the enhancement data.

18. A non-transitory machine-readable medium having a set of instructions which, when executed by a processor, cause the processor to perform a method for enhancing data to be used by a reasoning task, the method comprising:

receiving, by a data enhancement module, a first set of semantic data associated with the reasoning task;

generating, by the data enhancement module, a second set of semantic data by removing inconsistent data from the first set of semantic data, wherein the inconsistent data is identified from the first set of semantic data by a justification determination process; and

generating, by the data enhancement module, a third set of semantic data by adding enhancement data to the second set of semantic data, wherein the enhancement data is obtained based on the second set of semantic data by an abduction determination process.

19. The non-transitory machine-readable medium of claim 18, wherein the justification determination process comprises:

identifying one or more justifications based on the first set of semantic data, wherein each of the one or more justifications contains a plurality of elements selected from the first set of semantic data, the plurality of elements are inconsistent in an ontology, and removing one element from the plurality of elements makes the rest of the plurality of elements consistent in the ontology; and

identifying an inconsistent candidate based on the one or more justifications; and

appointing one or more elements in the inconsistent candidate as the inconsistent data removed from the first set of semantic data.

20. The non-transitory machine-readable medium of claim 18, wherein the abduction determination process comprises:

generating a plurality of abductions candidates based on an observation and the second set of semantic data;

for each abduction candidate selected from the plurality of abduction candidates, calculating a corresponding semantic relatedness score based on the abduction candidate and the observation;

selecting one or more enhancement candidates from the plurality of abduction candidates for having corresponding semantic relatedness scores that are above a predetermined threshold; and

adding the one or more enhancement candidates as the enhancement data to the second set of semantic data.