Systems and computer implemented methods for semantic data compression

1. A computer-implemented method for semantic compression and transmission of data, comprising:

receiving, at a first computer, a query from a second computer to transmit a plurality of artifacts to the second computer over a network, wherein the plurality of artifacts are from a corpus of documents;

queuing, at the first computer, the plurality of artifacts in response to the received query;

compressing, at the first computer, the plurality of queued artifacts, wherein compressing the plurality of queued artifacts comprises:

determining, at the first computer, for each of the plurality of queued artifacts, to send one of: an artifact, and only a link to the artifact based on semantic relatedness of queued artifacts to other queued artifacts, the determining resulting in a first set of artifacts to send, and a second set of artifacts to only send links;

wherein determining includes:

clustering the queued artifacts based on semantic similarity;

determining, for each cluster, a threshold based on respective semantic similarity scores of the artifacts;

assigning all artifacts in the cluster scoring above the threshold to the first set of artifacts;

transmitting, by the first computer, the first set of artifacts and links to the second set of artifacts over the network to the second computer.

2. The computer-implemented method of claim 1 , wherein the one or more network decision variables include one or more of: phrase index algorithm, cluster optimization, network analysis, geographic information system coordinate based tiling, geographic information system place name index, geographic information system shape file optimization, relationship driven optimization, automated National Image Transmission Format chipping, key length value video correlation, and query based machine learning optimization.

3. The computer-implemented method of claim 1 , wherein the one or more network decision variables are based on one or more of: relationships between textual elements in an artifact, and relationships between artifacts.

4. The computer-implemented method of claim 1 , wherein the relationships comprise semantic connections between two entities.

5. The computer-implemented method of claim 1 , further comprising:

ranking the queued artifacts by a semantic similarity score, wherein the semantic similarity score determines a strength of a relationship between artifacts; and

prioritizing transmission based on the semantic similarity score.

6. The computer-implemented method of claim 1 , wherein sending further comprises:

identifying an artifact of the plurality of artifacts that comprises an image; and

sending a segment of the image determined to be of relevance to the query.

7. The computer-implemented method of claim 1 , wherein sending further comprises:

identifying an artifact that comprises a video image; and

sending one or more frames of the video image determined to be of relevance to the query.

8. The computer-implemented method of claim 1 , wherein sending further comprises:

monitoring the hyperlink by a machine learning system to determine whether users open the hyperlink to the artifact; and

when the hyperlink is opened by a majority of the users to access the artifact, replacing the hyperlink with the artifact in a subsequent transmission.

9. The computer-implemented method of claim 8 , wherein a user requests transmission of the corresponding artifact for the hyperlink in a subsequent transmission.

10. The computer-implemented method of claim 1 , wherein the link comprises a hyperlink.

11. The computer-implemented method of claim 1 , wherein each link is sent with a statement for which known fact the artifact supports.

12. A cloud transfer service system for semantic data compression and transmission, comprising:

a processor;

a network interface coupled to the processor, wherein the network interface is communicatively coupled to a network;

a data storage system; and

a non-transitory memory coupled to the processor storing computer readable program instructions, wherein the computer readable program constructions configure the processor to perform the steps of:

receiving, at a first computer, a query from a second computer to transmit a plurality of artifacts to the second computer over a network, wherein the plurality of artifacts are from a corpus of documents;

queuing, at the first computer, the plurality of artifacts in response to the received query;

queuing, at the first computer, the plurality of artifacts in response to the received query;

compressing, at the first computer, the plurality of queued artifacts, wherein compressing the plurality of queued artifacts comprises:

determining, at the first computer, for each of the plurality of queued artifacts, to send one of: an artifact, and only a link to the artifact based on semantic relatedness of queued artifacts to other queued artifacts, the determining resulting in a first set of artifacts to send, and a second set of artifacts to only send links;

wherein determining includes:

clustering the queued artifacts based on semantic similarity;

determining, for each cluster, a threshold based on respective semantic similarity scores of the artifacts;

assigning all artifacts in the cluster scoring above the threshold to the first set of artifacts;

assigning all artifacts in the cluster scoring below the threshold to the second set of artifacts; and

transmitting, by the first computer, the first set of artifacts and links to the second set of artifacts over the network to the second computer.

13. The cloud transfer service system of claim 12 , wherein the one or more network decision variables include one or more of: phrase index algorithm, cluster optimization, network analysis, geographic information system coordinate based tiling, geographic information system place name index, geographic information system shape file optimization, relationship driven optimization, automated National Image Transmission Format chipping, key length value video correlation, and query based machine learning optimization.

14. The cloud transfer service system of claim 12 , wherein the one or more network decision variables are based on one or more of: relationships between textual elements in an artifact, and relationships between artifacts.

15. The cloud transfer service system of claim 12 , wherein the relationships comprise semantic connections between two entities.

16. The cloud transfer service system of claim 12 , further comprising:

ranking the queued artifacts by a semantic similarity score, wherein the semantic similarity score determines a strength of a relationship between artifacts; and

prioritizing transmission based on the semantic similarity score.

17. The cloud transfer service system of claim 12 , wherein sending further comprises:

identifying an artifact of the plurality of artifacts that comprises an image; and

sending a segment of the image determined to be of relevance to the query.

18. The cloud transfer service system of claim 12 , wherein sending further comprises:

identifying an artifact that comprises a video image; and

sending one or more frames of the video image determined to be of relevance to the query.

19. The cloud transfer service system of claim 12 , wherein sending further comprises:

monitoring the hyperlink by a machine learning system to determine whether users open the hyperlink to the artifact; and

when the hyperlink is opened by a majority of the users to access the artifact, replacing the hyperlink with the artifact in a subsequent transmission.

20. The cloud transfer service system of claim 19 , wherein a user requests transmission of the corresponding artifact for the hyperlink in a subsequent transmission.

21. The cloud transfer service system of claim 12 , wherein the link comprises a hyperlink.

22. The cloud transfer service system of claim 12 , wherein each link is sent with a statement for which known fact the artifact supports.