System and method for matching search requests and relevant data

• US 8,306,987 B2
• Filed: 03/04/2009
• Issued: 11/06/2012
• Est. Priority Date: 04/03/2008
• Status: Expired due to Fees

• Current Assignee: Ofer Ber, Ran Ber
• Original Assignee: Ofer Ber, Ran Ber
• Inventors: Ber, Ofer, Ber, Ran
• Primary Examiner(s): Beausoliel, Jr., Robert
• Assistant Examiner(s): ALLEN, NICHOLAS E
• Application Number:
  US12/379,892
• Publication Number:
  US 20090254543A1
• Time in Patent Office: 1,343 Days
• Field of Search: 707/752
• US Class Current: 707/752
• CPC Class Codes: G06F 16/334 Query execution G06F16/335 takes precedence
• CPC Class Codes: G06F 16/36 Creation of semantic tools, e.g. ontology or thesauri
• CPC Class Codes: G06F 16/90335 Query processing
• CPC Class Codes: G06F 16/907 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually

Abstract

A system and methods for matching between search requests and relevant data (web pages, online documents, essays, online text in general, images, video, footage etc.). The system comprises three components that can work separately or together and can be integrated with other search engine methods in order to further improve the relevancy of search results. The system can find similarity between different document and measure the distance (in similarity) between documents. The three components are: Context based understanding, comprising putting the documents in the context of aspects of the human knowledge external to the documents, Partial Sentence analysis and 100 percentage points to keyword/tag sets.

Claims

1. A computerized method of arrangement and representation of terms in context, comprising:

providing terms and relations from at least one source;

arranging said terms in a Human Knowledge Structure (HKS), said structure being a directed acyclic graph, wherein each one of said graph'"'"'s nodes consists of one term and the graph'"'"'s arcs comprise at least one of “

essence” and

“

contain”

relations between adjacent terms, wherein term ‘

A’

is the essence of term ‘

B’

, or term ‘

A’

contains term ‘

B’

;

providing a document;

identifying in the document frequent terms and marking them with associated respective initial weights on the HKS, said marked terms defining an initial weighted terms set for the document;

creating a final weighted terms set for the document by;

i. marking on the HKS all the terms connecting between said marked terms;

ii. calculating weights for said initial and connecting marked terms, based on said at least two relations in the HKS, whereby the marked terms define a weighted set of terms for the document; and

iii. selecting from said weighted marked terms set terms having weights greater than a predefined threshold, thereby defining a final weighted set of terms for the document; and

iv. linking said final weighted set of terms to the document.

2. The method of claim 1, wherein said initial weight of a term is its frequency value in the document.

3. The method of claim 1, wherein terms from the header, abstract or keywords set of the document are given higher initial weight values.

4. The method of claim 1, wherein said document comprises a set of terms used as metadata tags to describe the highlights of an object and wherein the initial weights associated with said set of terms sum up to a predefined sum.

5. The method of claim 4, wherein said object is selected from the group consisting of images, video clips, footages, songs, articles, papers and text documents.

6. The method of claim 1, additionally comprising adding to the final weighted set of terms, terms from the document that do not exist in the HKS, with initial weights.

7. The method of claim 1, additionally comprising matching between a set of search terms and a set of documents by:

identifying terms within the set of search terms;

retrieving the final weighted sets of terms linked to each of said documents;

calculating a final matching grade for each of said documents, based on the weights of terms in said final weighted set of terms of the document that are included in the set of search terms; and

ranking said documents according to said final matching grades.

8. The method of claim 7 wherein the set of search terms is a search string.

9. The method of claim 7, wherein said documents comprise documents provided by at least one of external search methodologies and search engine indexing systems, as results for said set of search terms.

10. The method of claim 7, wherein the set of search terms is a reference document and wherein the step of identifying terms comprises extracting terms from said final weighted set of terms of said reference document.

11. The method of claim 1, additionally comprising matching between a reference document and a set of documents, comprising:

mapping the final weighted set of terms of said reference document and of each document in said set of documents to a vector in ‘

n’

Euclidean space;

for each document in the set of documents, calculating the distance between the vector representing the document and the vector representing the reference document; and

ranking the documents from the set of documents by relevancy to the reference document, measured in distance units.

12. The method of claim 11 wherein said step of mapping is performed using only the terms with the highest weights.

13. A computerized method of matching between a search string and documents, comprising:

providing a search string comprising terms that form a partial sentence;

retrieving a set of documents comprising at least part of said partial sentence;

counting the number of exact occurrences of the partial sentence in each of the retrieved documents;

counting the number of occurrences of equivalent permutations of the partial sentence that do not conflict with the meaning of the partial sentence in each of the retrieved document;

counting the number of occurrences of close permutations of the partial sentence that do not conflict with the meaning of the partial sentence in each of the retrieved document;

for each retrieved document, associating scores to all said counted occurrences, based on the similarity between the occurrence and the partial sentence;

summing up the scores to a final score for each document; and

ranking the documents in a result list according to said final scores.

14. The method of claim 13, additionally comprising:

counting the number of occurrences of permutations of the partial sentence that conflict with the meaning of the partial sentence in each of the retrieved document; and

associating scores to all said counted occurrences of conflicting permutations in each of the retrieved document.

15. The method of claim 13, wherein said retrieving comprises retrieving documents provided by at least one of external search methodologies and search engine indexing systems, as results for said search string.

16. The method of claim 14, wherein if the number of non-conflicting occurrences of the partial sentence is zero and the number of conflicting occurrences is greater than zero, the document is filtered out.

17. A computerized system for arrangement and representation of terms in context, comprising:

a server;

at least one source of terms and relations;

communication means between said at least one source of terms and relations and said server;

a first storage device connected with said server;

means for storing said terms in said first storage device in a Human Knowledge Structure (HKS) being a directed acyclic graph, wherein each one of said graph'"'"'s nodes comprises one term and the graph'"'"'s arcs consist of at least one of “

essence” and

“

contain”

relations between adjacent terms, wherein term ‘

A’

is the essence of term ‘

B’

, or term ‘

A’

contains term ‘

B’

;

at least one source of documents;

communication means between said at least one source of documents and said server;

a second storage device connected with said server;

wherein said server comprises computerized means for;

receiving a document from said at least one source of documents;

identifying in the document frequent terms and marking them with associated respective initial weights on the HKS, said marked terms defining an initial weighted terms set for the document;

creating a final weighted terms set for the document by;

marking on the HKS all the terms connecting between said marked terms;

calculating weights for said initial and connecting marked, based on said at least two relations in the HKS, whereby the marked terms define a weighted set of terms for the document;

selecting from said weighted marked terms set terms having weights greater than a predefined threshold, thereby defining a final weighted set of terms for the document; and

linking said final weighted set of terms to the document; and

means for storing said linked final weighted set of terms in said second storage device.

18. The system of claim 17, additionally comprising:

at least one source of search terms; and

communication means between said at least one source of search terms and said server, wherein the server comprises additional computerized means for;

receiving at least one search term from said source of search terms ; and

matching between said at least one search term and a set of documents received from said at least one source of documents, said additional means comprising means for;

identifying terms within the at least one search term;

calculating a final matching grade for each of said documents, based on the weights of terms in the final weighted set of terms of the documents that are included in the at least one search term; and

ranking said documents according to said final matching grades.

19. A computerized system for matching between a search string and documents, comprising:

a server;

at least one source of search strings;

at least one source of documents;

communication means between said at least one source of search strings and said server; and

communication means between said at least one source of documents and said server, said server comprising computerized means for;

receiving from said at least one source of search strings a search string comprising terms that form a partial sentence;

retrieving from said at least one source of documents a set of documents comprising at least part of said partial sentence;

counting the number of exact occurrences of the partial sentence in each of the retrieved documents;

counting the number of occurrences of equivalent permutations of the partial sentence that do not conflict with the meaning of the partial sentence in each of the retrieved document;

counting the number of occurrences of close permutations of the partial sentence that do not conflict with the meaning of the partial sentence in each of the retrieved document;

for each retrieved document, associating scores to all said counted occurrences, based on the similarity between the occurrence and the partial sentence;

summing up the scores to a final score for each document; and

ranking the documents in a result list according to said final scores.

20. The system of claim 19, wherein said server additionally comprises computerized means for:

counting the number of occurrences of permutations of the partial sentence that conflict with the meaning of the partial sentence in each of the retrieved document; and

associating scores to all said counted occurrences of conflicting permutations in each of the retrieved document.

21. The system of claim 19, wherein said source of documents comprises documents provided by at least one of external search methodologies and search engine indexing systems, as results for said search string.