A method and apparatus are provided for recommending concepts from a first concept set in response to user selection of a first concept Ci by performing a natural language processing (NLP) analysis comparison of the vector representations of a first concept set of candidate concepts and a second concept set of user-explored concepts to determine a similarity measure corresponding to each candidate concept, and to select therefrom one or more of the candidate concepts for display as recommended concepts which are related to the one or more user-explored concepts from the navigation history for the user based on the similarity measure for each candidate concept.
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 identifying concepts, the method comprising: generating, by the system, at least a first concept set comprising one or more candidate concepts extracted from one or more content sources; generating, by the system, at least a second concept set comprising one or more user-explored concepts from a navigation history for the user; generating or retrieving, by the system, a vector representation of each candidate concept in the first concept set and each user-explored concept in the second concept set; performing, by the system, a natural language processing (NLP) analysis comparison of the vector representations of the candidate concepts in the first concept set to the vector representations of the user-explored concepts in the second concept set to determine a similarity measure corresponding to each candidate concept; and selecting, by the system, one or more of the candidate concepts for display as recommended concepts which are related to the one or more user-explored concepts from the navigation history for the user based on the similarity measure for each candidate concept. 2. The method of claim 1, wherein generating at least the first concept set comprises extracting a plurality of candidate concepts from a knowledge graph which connects concepts by edges of one or more types. 3. The method of claim 1, wherein generating at least the second concept set comprises capturing a concept sequence S=C1, . . . , Ck of k user-explored concepts, where k may be an initialized parameter that is programmable by the user. 4. The method of claim 1, wherein generating or retrieving the vector representation of each user-explored concept comprises modeling the user as a vector which represents an aggregated view of the user's interests or knowledge. 5. The method of claim 1, wherein performing the NLP analysis comprises analyzing a vector similarity function sim(Vi,Vj) between (1) a vector representation Vi of an average vector value computed from the one or more user-explored concepts and (2) one or more vectors Vj for each candidate concept in the first concept set. 6. The method of claim 1, wherein performing the NLP analysis comprises: analyzing, by the system, a vector similarity function sim(Vi,Vj) between (1) a vector representation Vi of a user-explored concept Ci in the second concept set and (2) one or more vectors Vj for each candidate concept in the first concept set to identify a sorted list of D concepts from the candidate concepts that are most strongly connected to the user-explored concept Ci, where D is a programmable parameter; and processing, by the system, the sorted list of D concepts to identify a concept C′ whose number of co-occurrences with the user-explored concept Ci in a window of U concepts from the candidate concepts is less than W, where U and W are programmable parameters. 7. The method of claim 1, further comprising displaying, by the system, the recommended concepts in response to the use moving a display cursor over a user-selected concept. 8. 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 identify concepts, wherein the set of instructions are executable to perform actions of: generating, by the system, at least a first concept set comprising one or more candidate concepts extracted from one or more content sources; generating, by the system, at least a second concept set comprising one or more user-explored concepts from a navigation history for the user; generating or retrieving, by the system, a vector representation of each candidate concept in the first concept set and each user-explored concept in the second concept set; performing, by the system, a natural language processing (NLP) analysis comparison of the vector representations of the candidate concepts in the first concept set to the vector representations of the user-explored concepts in the second concept set to determine a similarity measure corresponding to each candidate concept; and selecting, by the system, one or more of the candidate concepts for display as recommended concepts which are related to the one or more user-explored concepts from the navigation history for the user based on the similarity measure for each candidate concept. 9. The information handling system of claim 8, wherein the set of instructions are executable to generate at least the first concept set by extracting a plurality of candidate concepts from a knowledge graph which connects concepts by edges of one or more types. 10. The information handling system of claim 8, wherein the set of instructions are executable to generate at least the second concept set by capturing a concept sequence S=C1, . . . , Ck of k user-explored concepts, where k may be an initialized parameter that is programmable by the user. 11. The information handling system of claim 8, wherein the set of instructions are executable to generate the vector representation of each user-explored concept by modeling the user as a vector which represents an aggregated view of the user's interests or knowledge. 12. The information handling system of claim 8, wherein the set of instructions are executable to perform the NLP analysis by analyzing a vector similarity function sim(Vi,Vj) between (1) a vector representation Vi of an average vector value computed from the one or more user-explored concepts and (2) one or more vectors Vj for each candidate concept in the first concept set. 13. The information handling system of claim 8, wherein the set of instructions are executable to perform the NLP analysis by: analyzing, by the system, a vector similarity function sim(Vi,Vj) between (1) a vector representation Vi of a user-explored concept Ci in the second concept set and (2) one or more vectors Vj for each candidate concept in the first concept set to identify a sorted list of D concepts from the candidate concepts that are most strongly connected to the user-explored concept Ci, where D is a programmable parameter; and processing, by the system, the sorted list of D concepts to identify a concept C′ whose number of co-occurrences with the user-explored concept Ci in a window of U concepts from the candidate concepts is less than W, where U and W are programmable parameters. 14. The information handling system of claim 8, wherein the set of instructions are executable to display the recommended concepts in response to the use moving a display cursor over a user-selected concept. 15. 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 identify concepts by performing actions comprising: generating, by the system, at least a first concept set comprising one or more candidate concepts extracted from one or more content sources; generating, by the system, at least a second concept set comprising one or more user-explored concepts from a navigation history for the user; generating or retrieving, by the system, a vector representation of each candidate concept in the first concept set and each user-explored concept in the second concept set; performing, by the system, a natural language processing (NLP) analysis comparison of the vector representations of the candidate concepts in the first concept set to the vector representations of the user-explored concepts in the second concept set to determine a similarity measure corresponding to each candidate concept; and selecting, by the system, one or more of the candidate concepts for display as recommended concepts which are related to the one or more user-explored concepts from the navigation history for the user based on the similarity measure for each candidate concept. 16. The computer program product of claim 15, wherein generating at least the first concept set comprises extracting a plurality of candidate concepts from a knowledge graph which connects concepts by edges of one or more types. 17. The computer program product of claim 15, wherein generating at least the second concept set comprises capturing a concept sequence S=C1, . . . , Ck of k user-explored concepts, where k may be an initialized parameter that is programmable by the user. 18. The computer program product of claim 15, wherein generating the vector representation of each user-explored concept comprises modeling the user as a vector which represents an aggregated view of the user's interests or knowledge. 19. The computer program product of claim 15, wherein performing the NLP analysis comprises analyzing a vector similarity function sim(Vi,Vj) between (1) a vector representation Vi of an average vector value computed from the one or more user-explored concepts and (2) one or more vectors Vj for each candidate concept in the first concept set. 20. The computer program product of claim 15, wherein performing the NLP analysis comprises: analyzing, by the system, a vector similarity function sim(Vi,Vj) between (1) a vector representation Vi of a user-explored concept Ci in the second concept set and (2) one or more vectors Vj for each candidate concept in the first concept set to identify a sorted list of D concepts from the candidate concepts that are most strongly connected to the user-explored concept Ci, where D is a programmable parameter; and processing, by the system, the sorted list of D concepts to identify a concept C′ whose number of co-occurrences with the user-explored concept Ci in a window of U concepts from the candidate concepts is less than W, where U and W are programmable parameters. 21. The computer program product of claim 15, further comprising computer instructions that, when executed by the system, cause the system to display the recommended concepts in response to the use moving a display cursor over a user-selected concept.