An approach is provided for automatically generating and processing concept vectors by extracting concept sequences from one or more content sources and generating a first concept vector for a first concept by supplying the concept sequences as inputs to a vector learning component, such that the first concept vector comprises information interrelating the first concept to other concepts in the concept sequences which is inferred from the concept sequences.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, in an information handling system comprising a processor and a memory, for generating concept vectors, the method comprising: generating, by the system, concept sequences from one or more content sources by extracting a plurality of concepts to form each concept sequence; and generating, by the system, at least a first concept vector for a first concept by supplying the concept sequences as input to a vector learning component, such that the first concept vector comprises information interrelating the first concept to other concepts in the concept sequences which is inferred from the concept sequences. 2. The method of claim 1, wherein generating concept sequences comprises performing, by the system, a natural language processing (NLP) analysis of an annotated text source specializing in concepts to filter out words corresponding to non-concepts, leaving a plurality of concepts forming a concept sequence. 3. The method of claim 1, wherein generating concept sequences comprises performing, by the system, a natural language processing (NLP) analysis of an annotated text source specializing in concepts to replace one or more words forming each concept by a marked string representing the concept, leaving other words unchanged. 4. The method of claim 1, wherein generating concept sequences comprises performing, by the system, a random walk across concept nodes on a graph having edge weights, where a probability to proceed to a concept node depends on a connecting edge weight relative to edge weights of edges connecting to other concept nodes, thereby traversing a plurality of concepts forming a concept sequence. 5. The method of claim 1, wherein generating concept sequences comprises performing, by the system, a random walk across weighted concept nodes on a graph having edge weights, where a probability to proceed to a concept node depends on a connecting edge weight and on adjoining concept node's weight relative to weights of other connecting edges and adjoining concept nodes, thereby traversing a plurality of concepts forming a concept sequence. 6. The method of claim 1, wherein generating concept sequences comprises tracking, by the system, at least a first user's navigation behavior over a plurality of concepts to identify a plurality of concepts forming a concept sequence. 7. The method of claim 1, wherein generating at least the first concept vector comprises performing, by the system, machine learning processing to compute the first concept vector based on statistics of associations with the first concept in one or more of the concept sequences. 8. The method of claim 1, wherein generating at least the first concept vector comprises performing, by the system, a machine learning process to repeatedly scan the concept sequences to generate a vector representation for each concept in the sequence. 9. The method of claim 1, wherein generating concept sequences comprises filtering, by the system, the one or more concept sources to retain text related to a specified theme in a filtered content source before extracting the plurality of concepts from the filtered content source to form each concept sequence. 10. The method of claim 1, wherein generating concept sequences comprises: extracting a first plurality of concepts from a first set of concept annotations for the one or more content sources, and extracting a second plurality of concepts from a second set of concept annotations for the one or more content sources; and wherein generating at least the first concept vector comprises supplying the first plurality of concepts and second plurality of concepts as input to the vector learning component to generate the first concept vector and a second concept vector. 11. An information handling system comprising: one or more processors; a memory coupled to at least one of the processors; a set of instructions stored in the memory and executed by at least one of the processors to generate concept vectors, wherein the set of instructions are executable to perform actions of; generating, by the system, concept sequences from one or more content sources by extracting a plurality of concepts to form each concept sequence; and generating, by the system, a first concept vector for a first concept by supplying the concept sequences as input to a vector learning component, such that the first concept vector comprises information interrelating the first concept to other concepts in the concept sequences which is inferred from the concept sequences. 12. The information handling system of claim 11, wherein the set of instructions are executable to generate concept sequences by performing a natural language processing (NLP) analysis of an annotated text source specializing in concepts to filter out words corresponding to non-concepts, leaving a plurality of concepts forming a concept sequence. 13. The information handling system of claim 11, wherein the set of instructions are executable to generate concept sequences by performing a natural language processing (NLP) analysis of an annotated text source specializing in concepts to replace one or more words forming each concept by a marked string representing the concept, leaving other words unchanged. 14. The information handling system of claim 11, wherein the set of instructions are executable to generate concept sequences by performing a random walk across weighted concept nodes on a graph having edge weights, where a probability to proceed to a concept node depends on a connecting edge weight and an adjoining concept node's weight relative to other edges and concept nodes, thereby traversing a plurality of concepts forming a concept sequence. 15. The information handling system of claim 11, wherein the set of instructions are executable to generate concept sequences by tracking at least a first user's navigation behavior over a plurality of concepts to identify a plurality of concepts forming a concept sequence. 16. The information handling system of claim 11, wherein the set of instructions are executable to generate the first vector by performing machine learning processing to compute the first concept vector based on statistics of associations with the first concept in one or more of the concept sequences. 17. The information handling system of claim 11, wherein the set of instructions are executable to generate the first vector by performing a machine learning process to repeatedly scan the concept sequences to generate a vector representation for each concept in the sequence. 18. The information handling system of claim 11, wherein the set of instructions are executable to generate the first vector by performing a machine learning process to repeatedly scan the concept sequences to generate a vector representation for each concept in the sequence. 19. A computer program product stored in a computer readable storage medium, comprising computer instructions that, when executed by an information handling system, causes the system to generate concept vectors by performing actions comprising: generating, by the system, concept sequences from one or more content sources by extracting a plurality of concepts to form each concept sequence; and generating, by the system, a first concept vector for a first concept by supplying the concept sequences as input to a vector learning component, such that the first concept vector comprises information interrelating the first concept to other concepts in the concept sequences which is inferred from the concept sequences. 20. The computer program product of claim 19, wherein generating concept sequences comprises performing a natural language processing (NLP) analysis of an annotated text source specializing in concepts to filter out words corresponding to non-concepts, leaving a plurality of concepts forming a concept sequence. 21. The computer program product of claim 19, wherein generating concept sequences comprises performing a natural language processing (NLP) analysis of an annotated text source specializing in concepts to replace one or more words forming each concept by a marked string representing the concept, leaving other words unchanged. 22. The computer program product of claim 19, wherein generating concept sequences comprises performing a random walk across weighted concept nodes on a graph having edge weights, where a probability to proceed to a concept node depends on a connecting edge weight and an adjoining concept node's weight relative to other edges and concept nodes, thereby traversing a plurality of concepts forming a concept sequence. 23. The computer program product of claim 19, wherein generating concept sequences comprises tracking at least a first user's navigation behavior over a plurality of concepts to identify a plurality of concepts forming a concept sequence. 24. The computer program product of claim 19, wherein generating the first concept vector comprises performing machine learning processing to compute the first concept vector based on statistics of associations with the first concept in one or more of the concept sequences. 25. The computer program product of claim 19, wherein generating the first concept vector comprises performing a machine learning process to repeatedly scan the concept sequences to generate a vector representation for each concept in the sequence.