Dynamic semantic models having multiple indices

1. A method for improving the performance of managing data over a network by using a processor device, included with one or more network computers, to perform actions, comprising:

providing source data from one or more separate data sources to the one or more network computers;

providing a plurality of particular types of indices that are separate and optimized for one or more different content-types, wherein the particular types of indices include one or more of n-gram indices, temporal indices, or geo-spatial indices;

generating a raw data graph from the source data, wherein the structure of the raw data graph is based on the structure of the source data;

mapping one or more elements of the raw data graph to a concept graph;

generating one or more concept instances based on at least the concept graph, the raw data graph, and the source data;

generating one or more model-identifiers (MIDs) that correspond to the one or more concept instances, wherein the one or more MIDs include at least a path in the concept graph and one or more value keys that correspond to one or more portions of the source data;

indexing values from the source data that correspond to the one or more MIDs in the one or more indices that are selected from the plurality of indices based on a content-type of the source data that is associated with the one or more MIDs; and

responsive to a query, generating a result set that includes one or more result MIDs based on one or more indices of the plurality of indices, wherein a content-type of at least one portion of the query is employed to select the one or more indices used to generate the result set.

2. The method of claim 1 , wherein indexing the values from the source data that correspond to the one or more MIDs further comprises, generating one or more index records that include semantic equivalents of at least a value of the one or more MIDs.

3. The method of claim 1 , wherein the plurality of indices, further includes, at least one index that is optimized for a content-type of text, at least one index that is optimized for a content-type of time, and at least one index that is optimized for a content-type of geo-spatial information.

4. The method of claim 1 , wherein generating the raw data graph, further comprises:

providing the source data to one or more classifiers that are identified on a classifier registration list; and

modifying one or more raw data graph elements based on actions performed by the one or more classifiers.

5. The method of claim 1 , further comprising, computing one or more raw data graph elements based on the source data, wherein the value of the one or more raw data graph elements is absent from the source data.

6. The method of claim 1 , wherein mapping the one or more elements of the raw data graph to a concept graph, further comprises, determining one or more raw data graph elements based on one or more annotations that one or more classifiers added to the one or more raw data graph elements.

7. The method of claim 1 , further comprising, generating one or more additional queries based on at least a portion of the result set.

8. The method of claim 1 , further comprising, selecting the concept graph based on one or more ontologies.

9. A system for managing data over a network, comprising:

a modeling system server computer, comprising:

a transceiver that is operative to communicate over the network;

a memory that is operative to store at least instructions; and

a processor device that executes instructions that perform actions, including:

providing source data to the modeling system server computer from at least one separate data source;

providing a plurality of particular types of indices that are separate and optimized for one or more different content-types, wherein the particular types of indices include one or more of n-gram indices, temporal indices, or geo-spatial indices;

generating a raw data graph from the source data, wherein the structure of the raw data graph is based on the structure of the source data;

mapping one or more elements of the raw data graph to a concept graph;

generating one or more concept instances based on at least the concept graph, the raw data graph, and the source data;

generating one or more model-identifiers (MIDs) that correspond to the one or more concept instances, wherein the one or more MIDs include at least a path in the concept graph and one or more value keys that correspond to one or more portions of the source data;

indexing values from the source data that correspond to the one or more MIDs in one or more indices that are selected from the plurality of indices based on a content-type of the source data that is associated with the one or more MIDs; and

responsive to a query, generating a result set that includes one or more result MIDs based on one or more indices of the plurality of indices, wherein a content-type of at least one portion of the query is employed to select the one or more indices used to generate the result set; and

a source data server computer, comprising:

a transceiver that is operative to communicate over the network;

a memory that is operative to store at least instructions; and

a processor device that executes instructions that perform actions, including:

providing the source data to the modeling system server computer.

10. The system of claim 9 , wherein indexing the values from the source data that correspond to the one or more MIDs further comprises, generating one or more index records that include semantic equivalents of at least a value of the one or more MIDs.

11. The system of claim 9 , wherein the plurality of indices, further includes, at least one index that is optimized for a content-type of text, at least one index that is optimized for a content-type of time, and at least one index that is optimized for a content-type of geo-spatial information.

12. The system of claim 9 , wherein generating the raw data graph, further comprises:

providing the source data to one or more classifiers that are identified on a classifier registration list; and

modifying one or more raw data graph elements based on actions performed by the one or more classifiers.

13. The system of claim 9 , wherein the modeling system server processor device executes instructions that perform actions further comprising, computing one or more raw data graph elements based on the source data, wherein the value of the one or more raw data graph elements is absent from the source data.

14. The system of claim 9 , wherein mapping the one or more elements of the raw data graph to a concept graph, further comprises, determining one or more raw data graph elements based on one or more annotations that one or more classifiers added to the one or more raw data graph elements.

15. The system of claim 9 , wherein the modeling system server processor device executes instructions that perform actions further comprising, generating one or more additional queries based on at least a portion of the result set.

16. The system of claim 9 , wherein the modeling system server processor device executes instructions that perform actions further comprising, selecting the concept graph based on one or more ontologies.

17. A processor readable non-transitory storage media that includes instructions for improving the performance of managing data over a network, wherein execution of the instructions by a processor device performs actions, comprising:

providing source data to one or more network computers from one or more separate data sources;

providing a plurality of particular types of indices that are separate and optimized for one or more different content-types, wherein the particular types of indices include one or more of n-gram indices, temporal indices, or geo-spatial indices;

generating a raw data graph from the source data, wherein the structure of the raw data graph is based on the structure of the source data;

mapping one or more elements of the raw data graph to a concept graph;

generating one or more concept instances based on at least the concept graph, the raw data graph, and the source data;

generating one or more model-identifiers (MIDs) that correspond to the one or more concept instances, wherein the one or more MIDs include at least a path in the concept graph and one or more value keys that correspond to one or more portions of the source data;

indexing values from the source data that correspond to the one or more MIDs in one or more indices that are selected from the plurality of indices based on a content-type of the source data that is associated with the one or more MIDs; and

responsive to a query, generating a result set that includes one or more result MIDs based on one or more indices of the plurality of indices, wherein a content-type of at least one portion of the query is employed to select the one or more indices used to generate the result set.

18. The media of claim 17 , wherein indexing the values from the source data that correspond to the one or more MIDs further comprises, generating one or more index records that include semantic equivalents of at least a value of the one or more MIDs.

19. The media of claim 17 , wherein the plurality of indices, further includes, at least one index that is optimized for a content-type of text, at least one index that is optimized for a content-type of time, and at least one index that is optimized for a content-type of geo-spatial information.

20. The media of claim 17 , wherein generating the raw data graph, further comprises:

providing the source data to one or more classifiers that are identified on a classifier registration list; and

modifying one or more raw data graph elements based on actions performed by the one or more classifiers.

21. The media of claim 17 , further comprising, computing one or more raw data graph elements based on the source data, wherein the value of the one or more raw data graph elements is absent from the source data.

22. The media of claim 17 , wherein mapping the one or more elements of the raw data graph to a concept graph, further comprises, determining one or more raw data graph elements based on one or more annotations that one or more classifiers added to the one or more raw data graph elements.

23. The media of claim 17 , further comprising, generating one or more additional queries based on at least a portion of the result set.

24. A network computer for managing data over a network, comprising:

a transceiver that communicates over the network;

a memory that stores at least instructions; and

a processor device that executes instructions that perform actions, including:

providing source data to the network computer from one or more separate data sources;

providing a plurality of particular types of indices that are separate and optimized for one or more different content-types, wherein the particular types of indices include one or more of n-gram indices, temporal indices, or geo-spatial indices;

generating a raw data graph from the source data, wherein the structure of the raw data graph is based on the structure of the source data;

mapping one or more elements of the raw data graph to a concept graph;

generating one or more concept instances based on at least the concept graph, the raw data graph, and the source data;

generating one or more model-identifiers (MIDs) that correspond to the one or more concept instances, wherein the one or more MIDs include at least a path in the concept graph and one or more value keys that correspond to one or more portions of the source data;

indexing values from the source data that correspond to the one or more MIDs in one or more indices that are selected from the plurality of indices based on a content-type of the source data that is associated with the one or more MIDs; and

responsive to a query, generating a result set that includes one or more result MIDs based on one or more indices of the plurality of indices, wherein a content-type of at least one portion of the query is employed to select the one or more indices used to generate the result set.

25. The network computer of claim 24 , wherein indexing the values from the source data that correspond to the one or more MIDs further comprises, generating one or more index records that include semantic equivalents of at least the value of the one or more MIDs.

26. The network computer of claim 24 , wherein the plurality of indices, further includes, at least one index that is optimized for a content-type of text, at least one index that is optimized for a content-type of time, and at least one index that is optimized for a content-type of geo-spatial information.

27. The network computer of claim 24 , wherein generating the raw data graph, further comprises:

providing the source data to one or more classifiers that are identified on a classifier registration list; and

modifying one or more raw data graph elements based on actions performed by the one or more classifiers.

28. The network computer of claim 24 , wherein the processor device executes instructions that perform actions further comprising, computing one or more raw data graph elements based on the source data, wherein the value of the one or more raw data graph elements is absent from the source data.

29. The network computer of claim 24 , wherein mapping the one or more elements of the raw data graph to a concept graph, further comprises, determining one or more raw data graph elements based on one or more annotations that one or more classifiers added to the one or more raw data graph elements.

30. The network computer of claim 24 , wherein the processor device executes instructions that perform actions further comprising, generating one or more additional queries based on at least a portion of the result set.