COLLECTING DIGITAL ASSETS TO FORM A SEARCHABLE REPOSITORY

• US 20140195532A1
• Filed: 01/10/2013
• Published: 07/10/2014
• Est. Priority Date: 01/10/2013
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Dheap, Vijay, Whitley, Michael D.
• Application Number:
  US13/737,977
• Publication Number:
  US 20140195532A1
• Time in Patent Office: Days
• US Class Current: 707/736
• CPC Class Codes: G06F 16/901 Indexing; Data structures therefor; Storage structures for retrieval from the web G06F16/951

Abstract

A digital asset is identified, and a copy of the digital asset to store in a repository. Program code tokenizes plaintext into a grammar, wherein the plaintext is associated with the digital asset. If the digital asset is an image the program code is instructed to identify colors and shapes within the image, and also relationships between the image and other digital assets stored in the repository. Contextual information corresponding to the digital asset is generated by utilizing the plaintext that is tokenized into the grammar, wherein the contextual information includes parameter values representing the colors, shapes, and relationships identified. The repository is queried to retrieve one or more copies of other digital assets having contextual information that matches with the contextual information corresponding to the digital asset that is identified. The computer annotates the copy of the digital asset within the repository to form searchable metadata.

Claims

1. A computer implemented method for collecting digital assets to form a searchable repository, the method comprising the steps of:

identifying a digital asset;

extracting a copy of the digital asset to store in a repository;

tokenizing plaintext into a grammar, wherein the plaintext is associated with the digital asset;

determining if the digital asset is an image, wherein if the digital asset is an image then program code is instructed to identify colors and shapes within the image, and also relationships between the image and other digital assets stored in the repository, and wherein if the digital asset is an image the program code generates parameter values representing the colors, shapes, and relationships identified;

generating contextual information corresponding to the digital asset by utilizing the plaintext that is tokenized into the grammar, wherein the contextual information includes the parameter values representing the colors, shapes, and relationships identified;

querying the repository to retrieve one or more copies of other digital assets having contextual information that matches with the contextual information corresponding to the digital asset; and

annotating the copy of the digital asset within the repository to form searchable metadata.

2. The method of claim 1, wherein the digital asset that is identified is an image, a diagram, a flowchart, a video, or audio within e-mail that is sent to a recipient, within files that are uploaded to a file hosting server, or even within web content that is posted to a wiki.

3. The method of claim 1, wherein the program code identifies colors within the image by performing measurements that quantify an intensity of each of the colors, or by utilizing software libraries having program code that opens the image and obtains pixel values of the image.

4. The method of claim 1, wherein the program code identifies shapes within the image by overlaying predefined shapes on the image and computing a percentage of overlap, or by dividing the image into a plurality of subsections, and based on the subsections generating hash values that correspond to the image wherein the hash values that are generated are utilized to perform comparisons with other hash values corresponding to other images in the repository.

5. The method of claim 1, wherein the step of generating the contextual information comprises executing word sense disambiguation, named-entity recognition, anaphora resolution, and vector-based semantic analysis on the plaintext that is tokenized, and wherein contextual information includes the following:

words, phrases, and patterns identified within the plaintext, the parameter values generated representing colors, shapes, and relationships identified.

6. The method of claim 1, wherein the step of annotating the copy of the digital asset within the repository to form searchable metadata comprises:

associating a context, a topic, a relationship, license information, source information, and a tag as metadata attached to the copy of the digital asset;

associating any metadata supplied by an end-user to the copy of the digital asset;

generating a database table, within the repository, to store the metadata that is attached, lineage, and the metadata supplied by the end-user; and

associating the database table to the copy of the digital asset.

7. A computer program product for collecting digital assets to form a searchable repository, the computer program product comprising:

a computer readable storage medium and program instructions stored on the computer readable storage medium, the program instructions comprising;

program instructions to identify a digital asset;

program instructions to extract a copy of the digital asset to store in a repository;

program instructions to tokenize plaintext into a grammar, wherein the plaintext is associated with the digital asset;

program instructions to determine if the digital asset is an image, wherein if the digital asset is an image then program code is instructed to identify colors and shapes within the image, and also relationships between the image and other digital assets stored in the repository, and wherein if the digital asset is an image the program code generates parameter values representing the colors, shapes, and relationships identified;

program instructions to generate contextual information corresponding to the digital asset by utilizing the plaintext that is tokenized into the grammar, wherein the contextual information includes the parameter values representing the colors, shapes, and relationships identified;

program instructions to query the repository to retrieve one or more copies of other digital assets having contextual information that matches with the contextual information corresponding to the digital asset; and

program instructions to annotate the copy of the digital asset within the repository to form searchable metadata.

8. The computer program product of claim 7, wherein the digital asset that is identified is an image, a diagram, a flowchart, a video, or audio within e-mail that is sent to a recipient, within files that are uploaded to a file hosting server, or even within web content that is posted to a wiki.

9. The computer program product of claim 7, wherein the program code identifies colors within the image by performing measurements that quantify an intensity of each of the colors, or by utilizing software libraries having program code that opens the image and obtains pixel values of the image.

10. The computer program product of claim 7, wherein the program code identifies shapes within the image by overlaying predefined shapes on the image and computing a percentage of overlap, or by dividing the image into a plurality of subsections, and based on the subsections generating hash values that correspond to the image wherein the hash values that are generated are utilized to perform comparisons with other hash values corresponding to other images in the repository.

11. The computer program product of claim 7, wherein the step of generating the contextual information comprises executing word sense disambiguation, named-entity recognition, anaphora resolution, and vector-based semantic analysis on the plaintext that is tokenized, and wherein contextual information includes the following:

words, phrases, and patterns identified within the plaintext, the parameter values generated representing colors, shapes, and relationships identified.

12. The computer program product of claim 7, wherein the step of annotating the copy of the digital asset within the repository to form searchable metadata comprises:

associating a context, a topic, a relationship, license information, source information, and a tag as metadata attached to the copy of the digital asset;

associating any metadata supplied by an end-user to the copy of the digital asset;

generating a database table, within the repository, to store the metadata that is attached, lineage, and the metadata supplied by the end-user; and

associating the database table to the copy of the digital asset.

13. A computer system for collecting digital assets to form a searchable repository, the computer system comprising:

one or more processors, one or more computer readable memories, one or more computer readable storage media, and program instructions stored on the one or more storage media for execution by the one or more processors via the one or more memories, the program instructions comprising;

program instructions to identify a digital asset;

program instructions to extract a copy of the digital asset to store in a repository;

program instructions to tokenize plaintext into a grammar, wherein the plaintext is associated with the digital asset;

program instructions to determine if the digital asset is an image, wherein if the digital asset is an image then program code is instructed to identify colors and shapes within the image, and also relationships between the image and other digital assets stored in the repository, and wherein if the digital asset is an image the program code generates parameter values representing the colors, shapes, and relationships identified;

program instructions to generate contextual information corresponding to the digital asset by utilizing the plaintext that is tokenized into the grammar, wherein the contextual information includes the parameter values representing the colors, shapes, and relationships identified;

program instructions to query the repository to retrieve one or more copies of other digital assets having contextual information that matches with the contextual information corresponding to the digital asset; and

program instructions to annotate the copy of the digital asset within the repository to form searchable metadata.

14. The computer system of claim 13, wherein the digital asset that is identified is an image, a diagram, a flowchart, a video, or audio within e-mail that is sent to a recipient, within files that are uploaded to a file hosting server, or even within web content that is posted to a wiki.

15. The computer system of claim 13, wherein the program code identifies colors within the image by performing measurements that quantify an intensity of each of the colors, or by utilizing software libraries having program code that opens the image and obtains pixel values of the image.

16. The computer system of claim 13, wherein the program code identifies shapes within the image by overlaying predefined shapes on the image and computing a percentage of overlap, or by dividing the image into a plurality of subsections, and based on the subsections generating hash values that correspond to the image wherein the hash values that are generated are utilized to perform comparisons with other hash values corresponding to other images in the repository.

17. The computer system of claim 13, wherein the step of generating the contextual information comprises executing word sense disambiguation, named-entity recognition, anaphora resolution, and vector-based semantic analysis on the plaintext that is tokenized, and wherein contextual information includes the following:

words, phrases, and patterns identified within the plaintext, the parameter values generated representing colors, shapes, and relationships identified.

18. The computer system of claim 13, wherein the step of annotating the copy of the digital asset within the repository to form searchable metadata comprises:

associating a context, a topic, a relationship, license information, source information, and a tag as metadata attached to the copy of the digital asset;

associating any metadata supplied by an end-user to the copy of the digital asset;

generating a database table, within the repository, to store the metadata that is attached, lineage, and the metadata supplied by the end-user; and

associating the database table to the copy of the digital asset.