Team analytics context graph generation and augmentation

1. A method for team analytics context graph generation and augmentation, comprising:

extracting, by a processor, a set of relevant features from a received message;

predicting, by the processor, a context graph corresponding to the received message being sparse based on the extracted relevant features, a context of the received message being indeterminable from the context graph in response to the context graph being sparse, wherein the context graph is a sparse context graph in response to a number of nodes and associated edges of the context graph corresponding to the received message being below a preset threshold number of nodes and associated edges;

generating, by the processor, an augmented context graph in response to the context graph being predicted to be sparse, wherein the augmented context graph is generated by adding a quantity of nodes and associated edges to the sparse context graph such that the context of the received message is determinable from the augmented context graph, wherein generating the augmented context graph comprises:

extracting data from the received message, the data comprising subjects and terms in the received message;

automatically generating a set of queries using the subjects and terms extracted from the received message to generate a list of selected users with a similar expertise or content corresponding to the subjects and terms;

generating the list of selected users based on the set of queries;

determining connections between each selected user of the list of selected users, wherein determining connections comprises searching a cache comprising previously generated context graphs and sub-graphs;

translating the selected users and connections between the selected users into nodes and edges of the augmented context graph;

building the augmented context graph in a data structure of parent-child relationships from translating the selected users and connections into the nodes and edges of the augmented context graph, the context of the received message being determinable from the augmented context graph; and

presenting, by the processor, the augmented context graph.

2. The method of claim 1 , wherein predicting the context graph corresponding to the message being sparse comprises:

determining the number of nodes and associated edges of the context graph corresponding to the received message based on the extracted relevant features; and

comparing the number of nodes and associated edges of the context to the preset threshold number of nodes and associated edges, wherein the context graph is the sparse context graph in response to the predicted number of nodes and associated edges of the context graph being less than the preset threshold number of nodes and associated edges.

3. The method of claim 1 , wherein generating the augmented context graph comprises:

retrieving references to prior communications from the extracted data; and

adding the references to a list for augmenting the context graph.

4. The method of claim 3 , wherein generating the augmented context graph further comprises extracting From/To patterns from the prior communications for generating the augmented context graph.

5. The method of claim 1 , further comprising querying each user of the list of selected users to determine a connection or relationship to one or more other users of the list of selected users.

6. The method of claim 1 , wherein nodes in the augmented context graph that include a direct contextual significance are distinguished from nodes that include an indirect contextual significance.

7. The method of claim 6 , wherein at least nodes that include a direct contextual significance comprises an interactive feature, wherein information associated with the node is presented in response to activation of the interactive feature.

8. The method of claim 6 , further comprising presenting the augmented context graph in a plurality of fragments in response to no edge being available between a first set of nodes and a second set of nodes.

9. The method of claim 1 , further comprising caching the relevant features and associated information from an evaluation of the relevant features and caching the augmented context graph.

10. The method of claim 9 , further comprising:

continuously monitoring new messages being received;

automatically evaluating the relevant features of each new message; and

updating the relevant features and associated information based on the evaluation for presenting a new context graph or augmented contextual graph.

11. A system for team analytics context graph generation and augmentation, comprising:

a processor;

a module operating on the processor for team analytics context graph generation and augmentation, the module being configured to perform a set of functions comprising:

extracting a set of relevant features from a received message;

predicting a context graph corresponding to the received message being sparse based on the extracted relevant features, a context of the received message being indeterminable from the context graph in response to the context graph being sparse, wherein the context graph is a sparse context graph in response to a number of nodes and associated edges of the context graph corresponding to the received message being below a preset threshold number of nodes and associated edges;

generating an augmented context graph in response to the context graph being predicted to be sparse, wherein the augmented context graph is generated by adding a quantity of nodes and associated edges to the sparse context graph such that the context of the received message is determinable from the augmented context graph, wherein generating the augmented context graph comprises:

extracting data from the received message, the data comprising subjects and terms in the received message;

automatically generating a set of queries using the subjects and terms extracted from the received message to generate a list of selected users with a similar expertise or content corresponding to the subjects and terms;

generating the list of selected users based on the set of queries;

determining connections between each selected user of the list of selected users, wherein determining connections comprises searching a cache comprising previously generated context graphs and sub-graphs;

translating the selected users and connections between the selected users into nodes and edges of the augmented context graph;

building the augmented context graph in a data structure of parent-child relationships from translating the selected users and connections into the nodes and edges of the augmented context graph, the context of the received message being determinable from the augmented context graph; and

presenting the augmented context graph.

12. The system of claim 11 , wherein the set of functions further comprises:

querying each user of the list of selected users to determine a connection or relationship to each other user of the list of selected users.

13. The system of claim 11 , wherein the set of functions further comprises:

caching the augmented context graph;

continuously monitoring new messages being received;

automatically evaluating the relevant features of each new message; and

updating the relevant features and associated information based on the evaluation for presenting a new context graph or augmented contextual graph.

14. A computer program product for team analytics context graph generation and augmentation, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, wherein the computer readable storage medium is not a transitory medium per se, the program instructions being executable by a device to cause the device to perform a method comprising:

extracting a set of relevant features from a received message;

predicting a context graph corresponding to the received message being sparse based on the extracted relevant features, a context of the received message being indeterminable from the context graph in response to the context graph being sparse, wherein the context graph is a sparse context graph in response to a number of nodes and associated edges of the context graph corresponding to the received message being below a preset threshold number of nodes and associated edges;

generating an augmented context graph in response to the context graph being predicted to be sparse, wherein the augmented context graph is generated by adding a quantity of nodes and associated edges to the sparse context graph such that the context of the received message is determinable from the augmented context graph, wherein generating the augmented context graph comprises:

extracting data from the received message, the data comprising subjects and terms in the received message;

automatically generating a set of queries using the subjects and terms extracted from the received message to generate a list of selected users with a similar expertise or content corresponding to the subjects and terms;

generating the list of selected users based on the set of queries;

determining connections between each selected user of the list of selected users, wherein determining connections comprises searching a cache comprising previously generated context graphs and sub-graphs;

translating the selected users and connections between the selected users into nodes and edges of the augmented context graph;

building the augmented context graph in a data structure of parent-child relationships from translating the selected users and connections into the nodes and edges of the augmented context graph, the context of the received message being determinable from the augmented context graph; and

presenting the augmented context graph.

15. The computer program product of claim 14 , wherein the method further comprises:

caching the augmented context graph;

continuously monitoring new messages being received;

automatically evaluating the relevant features of each new message; and

updating the relevant features and associated information based on the evaluation for presenting a new context graph or augmented contextual graph.