Concept mining and concept discovery-semantic search tool for large digital databases

• US 20050149510A1
• Filed: 01/05/2005
• Published: 07/07/2005
• Est. Priority Date: 01/07/2004
• Status: Abandoned Application

• Current Assignee: Uri Shafrir
• Original Assignee: Uri Shafrir
• Inventors: Shafrir, Uri
• Application Number:
  US11/028,679
• Publication Number:
  US 20050149510A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/3338 Query expansion
• CPC Class Codes: G06F 16/367 Ontology

Abstract

The conceptual content of a discipline may be mapped by systematically identifying hierarchical and lateral links among lexical labels of the discipline. The hierarchical links connect a super-ordinate (or “parent”) concept to its sub-ordinate (or “child”) concepts. The lateral links provide relations between the concepts. Lexical labels do not accept synonyms; however, relations do accept synonyms. Conceptual content of documents in a digital text database may be identified, and documents may be subsequently sorted and ranked by their conceptual content.

Claims

1. A method comprising:

searching a digital text database for results that include a super-ordinate concept in a particular context by specifying;

a) a lexical label of said super-ordinate concept,

b) lexical labels of two or more sub-ordinate concepts that co-occur when said super-ordinate concept is present, and

c) said particular context,

wherein searching said database takes into account that said lexical labels do not accept synonyms.

2. The method of claim 1, wherein searching said database for results further includes specifying at least one relation between said lexical labels and specifying that said results can include synonyms of said at least one relation.

3. The method of claim 1, wherein searching said database for results further includes specifying one or more additional representations of said particular context.

4. A method comprising:

searching a digital text database for initial results that include a super-ordinate concept in a particular context by specifying a lexical label of said super-ordinate concept and by specifying said particular context;

identifying from said initial results lexical labels of two or more sub-ordinate concepts that co-occur when said super-ordinate concept is present; and

searching said database for refined results by specifying a) said lexical label of said super-ordinate concept, b) said lexical labels of said two or more sub-ordinate concepts, and c) said particular context.

5. The method of claim 4, wherein identifying said lexical labels of said two or more sub-ordinate concepts includes at least:

displaying portions of text of said initial results that precede said lexical label of said super-ordinate concept;

displaying portions of text of said initial results that follow said lexical label of said super-ordinate concept; and

counting a frequency of words in said displayed portions of text according to one or more criteria.

6. The method of claim 5, further comprising:

identifying from said refined results lexical labels of additional sub-ordinate concepts that co-occur when said super-ordinate concept is present; and

searching said database for further refined results by specifying a) said lexical label of said super-ordinate concept, b) said lexical labels of said two or more sub-ordinate concepts, c) said lexical labels of said additional sub-ordinate concepts and d) said particular context.

7. The method of claim 5, further comprising:

rank-ordering said refined results according to said frequency.

8. The method of claim 4, further comprising:

identifying from said initial results at least one relation between said lexical labels,

wherein searching said database for refined results includes specifying said at least one relation and specifying that said refined results can include synonyms of said at least one relation.

9. The method of claim 4, wherein specifying said particular context includes specifying one or more additional representations of said particular context.

10. A method comprising:

mapping conceptual content of a discipline by systematically identifying hierarchical and lateral links among lexical labels of said discipline.

11. The method of claim 10, further comprising:

graphically representing said lexical labels as nodes in a multi-dimensional lattice and graphically representing said links as connections among said nodes.

12. An article having stored thereon instructions, which when executed by a computing platform, result in:

presenting a user-interface to enable specification of search terms including at least;

a) a lexical label of said super-ordinate concept,

b) lexical labels of two or more sub-ordinate concepts that must co-occur for said super-ordinate concept to be present, and

c) said particular context; and

providing said search terms to a search engine, taking into account that said lexical labels do not accept synonyms.

13. The article of claim 12, wherein said search terms also include at least one relation between said lexical labels, and providing said search terms to said search engine takes into account that said relation does accept synonyms.

14. The article of claim 12, wherein said search terms also include one or more additional representations of said particular context.

15. An article having stored thereon instructions, which when executed by a computing platform, result in:

presenting a user-interface to enable specification of search terms including at least;

a) a lexical label of said super-ordinate concept, and

b) said particular context;

providing said search terms to a search engine, taking into account that said lexical label does not accept synonyms, to generate results;

displaying portions of text of said results that precede said lexical label of said super-ordinate concept;

displaying portions of text of said results that follow said lexical label of said super-ordinate concept; and

counting a frequency of words in said displayed portions of text according to one or more criteria.

16. The article of claim 15, wherein said user-interface further enables specification as additional search terms lexical labels of two or more sub-ordinate concepts that must co-occur for said super-ordinate concept to be present.

17. The article of claim 15, wherein said instructions, when executed by said computing platform, further result in rank-ordering said results according to said frequency.