Business intelligence data models with concept identification using language-specific clues

• US 10,002,126 B2
• Filed: 09/17/2014
• Issued: 06/19/2018
• Est. Priority Date: 03/15/2013
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Grosset, Robin N., Rais-Ghasem, Mohsen, Watts, Graham A., Wei, Qing
• Primary Examiner(s): VO, HUYEN X
• Application Number:
  US14/489,251
• Publication Number:
  US 20150006160A1
• Time in Patent Office: 1,371 Days
• Field of Search: 704 1- 10, 704230-257, 704270, 7042701
• CPC Class Codes: G06F 16/353 into predefined classes
• CPC Class Codes: G06F 40/258 Heading extraction; Automatic titling; Numbering
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06F 40/40 Processing or translation of natural language natural language analysis G06F40/20; semantic analysis G06F40/30

Abstract

Techniques are described for modeling information from a data source. In one example, a method for modeling information from a data source includes comparing, with one or more computing devices, a data item heading from the data source with concept keywords in a concept library, the concept library comprising a plurality of concepts and one or more of the concept keywords in at least one language associated with each of one or more of the concepts. The method further includes identifying, with one or more computing devices, one or more matches between the data item heading and one or more concept keywords associated with a particular concept from among the concepts comprised in the concept library. The method further includes identifying, with one or more computing devices, the data item heading as being associated with the particular concept.

Claims

1. A method for data modeling comprising:

comparing, with one or more processors of a computing system configured to perform data modeling, a data item heading from a data source with concept keywords in a concept library, wherein the data item heading is associated with a set of data items in the data source, and the concept library comprising a plurality of concepts and one or more of the concept keywords in at least one language associated with each of one or more of the concepts, wherein the concept library is tailored to a particular business ontology of a particular business;

identifying, with the one or more processors, one or more matches between the data item heading and one or more concept keywords associated with a particular concept from among the concepts comprised in the concept library;

identifying, with the one or more processors, the data item heading as being associated with the particular concept;

validating, with the one or more processors, the one or more matches between the data item heading and the one or more concept keywords associated with the particular concept;

applying, with the one or more processors, a concept tag in association with the data item heading, wherein the concept tag indicates the particular concept with which the data item heading is identified as being associated; and

outputting, with the one or more processors, for display, the concept tag in association with the data item heading to a business intelligence web application, wherein the business intelligent web application uses the concept tag to determine a data analytics interface output mode for creation of a data visualization of one or more variables in relation to entries corresponding to the particular concept,

wherein validating the one or more matches between the data item heading and the one or more concept keywords associated with the particular concept comprises assigning priority to the particular concept based on a number of matches between the one or more concept keywords associated with the particular concept in the particular business ontology and the data item heading, and

wherein assigning priority to the particular concept based on the number of matches between the one or more concept keywords associated with the particular concept in the particular business ontology and the data item heading comprises identifying if the one or more concept keywords associated with the particular concept provides a highest number of matches with the data item heading of any concept in the concept library.

2. The method of claim 1, wherein comparing the data item heading with the concept keywords in the concept library further comprises:

identifying a primary language of the data item heading, wherein the one or more of the concept keywords in the at least one language comprise one or more concept keywords in the primary language; and

comparing the data item heading with the one or more concept keywords in the primary language.

3. The method of claim 1, wherein comparing the data item heading with the concept keywords in the concept library further comprises:

identifying one or more tokens based on the data item heading; and

comparing the one or more tokens based on the data item heading with the one or more concept keywords in the concept library.

4. The method of claim 3, wherein identifying the one or more tokens based on the data item heading comprises applying a trie-based tokenizer to the data item heading.

5. The method of claim 1, wherein comparing the data item heading with the concept keywords in the concept library further comprises:

identifying one or more alternate strings based on a word stem of the data item heading; and

comparing the one or more alternate strings based on the word stem of the data item heading with the one or more concept keywords in the concept library.

6. The method of claim 1, wherein comparing the data item heading with the concept keywords in the concept library further comprises:

identifying one or more near matches of the data item heading; and

comparing the one or more near matches of the data item heading with the one or more concept keywords in the concept library.

7. The method of claim 1, wherein comparing the data item heading with the concept keywords in the concept library further comprises:

identifying one or more alternate language clues associated with the data item heading, wherein the one or more alternate language clues comprise content in one or more alternate languages besides a primary language of the data item heading; and

comparing the one or more alternate language clues associated with the data item heading with the one or more concept keywords in the concept library.

8. The method of claim 7, wherein the primary language is a language other than English, and the one or more alternate languages comprise English.

9. The method of claim 1, wherein identifying the one or more matches between the data item heading and the one or more concept keywords associated with the particular concept further comprises:

validating the one or more matches between the data item heading and the one or more concept keywords associated with the particular concept against additional evidence from the data source.

10. The method of claim 9, wherein the data item heading is a first data item heading, and wherein the additional evidence from the data source comprises one or more of:

values of data associated with the first data item heading, patterns of data associated with the first data item heading, and additional data item headings comparable to the first data item heading.

11. The method of claim 1, further comprising:

identifying a data analytics interface output mode that corresponds to the particular concept; and

outputting the data analytics interface output mode identified as corresponding to the particular concept.

12. The method of claim 11, wherein the particular concept comprises time, and the data analytics interface output mode identified as corresponding to the particular concept comprises a data visualization of one or more variables in relation to time.

13. The method of claim 11, wherein the particular concept comprises names, and the data analytics interface output mode identified as corresponding to the particular concept comprises a data visualization of one or more variables in relation to entries corresponding to the names.

14. The method of claim 1, wherein the data item heading comprises one or more of a column heading, a row heading, a sheet name, a graph caption, a file name, and a document title from the data source.