The present disclosure includes techniques pertaining to computer automated learning management systems and methods. In one embodiment, a system is disclosed where information is represented in a learning graph. In one embodiment, a framework may be used to access different algorithms for identifying customized learning content for a user. In another embodiment, the present disclosure includes techniques for analyzing content and incorporating content into an organizational glossary.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: storing data in a learning graph, the learning graph comprising nodes and edges, wherein a plurality of content nodes represent learning content and a plurality of person nodes represent individuals and wherein edges between nodes comprise association types defining particular relationships between particular nodes, wherein a first edge between a particular content node and a particular person node establishes a first association type, the first edge indicating that learning content corresponding to the particular content node has been consumed by an individual corresponding to the particular person node; adaptively deriving, from the learning graph, a customized learning path for the particular person node, the customized learning path specifying a plurality of content nodes in the learning graph; and associating the customized learning path with the particular person node to specify content to be consumed by particular individuals corresponding to the particular person node. 2. The method of claim 1 wherein the learning graph further comprises a plurality of learning collection nodes, wherein the learning collection nodes have a plurality of edges to content nodes. 3. The method of claim 2 wherein the learning collection nodes have a plurality of subtypes, wherein a first subtype of the plurality of subtypes specifies that a particular learning collection node is a customized learning path derived from the learning graph. 4. The method of claim 2 wherein the learning graph further comprises a plurality of learning goal nodes, wherein one or more person nodes have one or more edges to one or more learning goal nodes. 5. The method of claim 4 wherein one or more learning goal nodes have one or more edges to one or more learning collection nodes. 6. The method of claim 1 wherein a subset of the content nodes have a plurality of subtypes, wherein a first subtype of the plurality of subtypes specifies that a particular content node is a learning item. 7. The method of claim 1 wherein one or more edges comprise a weight. 8. The method of claim 1 wherein adaptively deriving, from the learning graph, the customized learning path for the particular person node comprises: traversing the learning graph from a particular one of the one or more person nodes to a learning goal node, the learning goal node specifying information to be learned; traversing the learning graph to find a plurality of relevant content to the learning goal based on one or more predefined content identification strategy algorithms; and compiling the relevant content into said customized learning path. 9. The method of claim 8 wherein compiling the relevant content into the customized learning path comprises: creating a learning path node in the learning graph; creating an edge between the learning path node and each content node corresponding to the relevant content; and coupling the learning path node to the particular person node. 10. The method of claim 8 wherein at least one of the predefined content identification strategy algorithms analyzes nodes and edges in the learning graph. 11. The method of claim 8 wherein at least one of the predefined content identification strategy algorithms comprises statistical operations applied against data stored in the learning graph. 12. A computer system comprising: a processor; and a non-transitory computer readable medium having stored thereon one or more programs, which when executed by the processor, causes the processor to: store data in a learning graph, the learning graph comprising nodes and edges, wherein a plurality of content nodes represent learning content and a plurality of person nodes represent individuals and wherein edges between nodes comprise association types defining particular relationships between particular nodes, wherein a first edge between a particular content node and a particular person node establishes a first association type, the first edge indicating that learning content corresponding to the particular content node has been consumed by an individual corresponding to the particular person node; adaptively derive, from the learning graph, a customized learning path for the particular person node, the customized learning path specifying a plurality of content nodes in the learning graph; and associate the customized learning path with the particular person node to specify content to be consumed by particular individuals corresponding to the particular person node. 13. The computer system of claim 12 wherein the learning graph further comprises a plurality of learning collection nodes, wherein the learning collection nodes have a plurality of edges to content nodes. 14. The computer system of claim 12 wherein the learning graph further comprises a plurality of learning goal nodes, wherein one or more person nodes have one or more edges to one or more learning goal nodes. 15. The computer system of claim 12 wherein said adaptively derive, from the learning graph, the customized learning path for the particular person node comprises: traverse the learning graph from a particular one of the one or more person nodes to a learning goal node, the learning goal node specifying information to be learned; traverse the learning graph to find a plurality of relevant content to the learning goal based on one or more predefined content identification strategy algorithms; and compile the relevant content into said customized learning path. 16. The computer system of claim 15 wherein compile the relevant content into the customized learning path comprises: create a learning path node in the learning graph; create an edge between the learning path node and each content node corresponding to the relevant content; and couple the learning path node to the particular person node. 17. A non-transitory computer readable storage medium storing one or more programs, the one or more programs comprising instructions for: storing data in a learning graph, the learning graph comprising nodes and edges, wherein a plurality of content nodes represent learning content and a plurality of person nodes represent individuals and wherein edges between nodes comprise association types defining particular relationships between particular nodes, wherein a first edge between a particular content node and a particular person node establishes a first association type, the first edge indicating that learning content corresponding to the particular content node has been consumed by an individual corresponding to the particular person node; adaptively deriving, from the learning graph, a customized learning path for the particular person node, the customized learning path specifying a plurality of content nodes in the learning graph; and associating the customized learning path with the particular person node to specify content to be consumed by particular individuals corresponding to the particular person node. 18. The non-transitory computer readable storage medium of claim 17 wherein the learning graph further comprises a plurality of learning collection nodes, wherein the learning collection nodes have a plurality of edges to content nodes and wherein the learning graph further comprises a plurality of learning goal nodes, wherein one or more person nodes have one or more edges to one or more learning goal nodes. 19. The non-transitory computer readable storage medium of claim 17 wherein said adaptively deriving, from the learning graph, the customized learning path for the particular person node comprises: traversing the learning graph from a particular one of the one or more person nodes to a learning goal node, the learning goal node specifying information to be learned; traversing the learning graph to find a plurality of relevant content to the learning goal based on one or more predefined content identification strategy algorithms; and compiling the relevant content into said customized learning path. 20. The non-transitory computer readable storage medium of claim 19 wherein compiling the relevant content into the customized learning path comprises: creating a learning path node in the learning graph; creating an edge between the learning path node and each content node corresponding to the relevant content; and coupling the learning path node to the particular person node.