A method and apparatus are provided for generating concept hierarchies in response to a user request to produce a hierarchy of concepts from a first concept set by performing a natural language processing (NLP) analysis comparison of the vector representations of the concepts in the first concept set to determine a similarity measure for each pair of distinct concepts Ci and Cj in the first concept set, and to construct therefrom a concept hierarchy based on one or more specified hierarchy parameters and the similarity measure for each pair of distinct concepts Ci and Cj in the first concept set.
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 hierarchies, the method comprising: generating, by the system, at least a first concept set comprising one or more concepts extracted from one or more content sources; receiving, by the system, a user request to produce a hierarchy of concepts from the first concept set using one or more specified hierarchy parameters; generating or retrieving, by the system, a vector representation of each of the concepts in the first concept set; performing, by the system, a natural language processing (NLP) analysis comparison of the vector representation of each of the concepts in the first concept set to determine a similarity measure for each pair of distinct concepts Ci and Cj in the first concept set; and constructing, by the system, a concept hierarchy based on the one or more specified hierarchy parameters and the similarity measure for each pair of distinct concepts Ci and Cj in the first concept set. 2. The method of claim 1, further comprising displaying, by the system, the concept hierarchy to visually present inter-relations between concepts from the first concept set. 3. The method of claim 1, further comprising displaying, by the system, the concept hierarchy to visually present a hierarchical structure conveying concept grouping of concepts from the first concept set to enable user navigation over the first concept set. 4. The method of claim 1, wherein one or more of the specified hierarchy parameters are specified in the user request. 5. The method of claim 1, wherein one or more of the specified hierarchy parameters are specified default parameters. 6. The method of claim 1, wherein performing the NLP analysis comprises analyzing a vector similarity function sim(Vi, Vj) between vectors Vi, Vj representing each pair of distinct concepts Ci and Cj in the first concept set. 7. The method of claim 6, wherein analyzing the vector similarity function sim(Vi, Vj) comprises, for each concept Ci for i=1 . . . N: computing, by the system, the similarity measure corresponding to said concept Ci as a cosine distance measure between each vector pair Vi, Vj for j=1 . . . N, i≠j; and selecting a distinct, unconnected concept Cj having a maximum cosine distance measure with the concept Ci. 8. The method of claim 1, wherein constructing the concept hierarchy comprises using a bottom-up method to iteratively build a concept graph by selecting distinct, unconnected concepts Ci, Cj from the first concept set based on a maximal similarity measure and identifying a first concept as a hierarchy root which has a maximal number of occurrences in the first concept set. 9. The method of claim 1, wherein constructing the concept hierarchy comprises using agglomerative hierarchical vector clustering based on a specified similarity metric and one or more of standard linkage criteria for merging candidate clusters. 10. The method of claim 9, further comprising switching among a plurality of alternative standard linkage criteria according to a size, shape, and validity measure of intermediate clusters. 11. The method of claim 1, wherein constructing the concept hierarchy comprises using a top-down/frequency method to sort the one or more concepts in the first concept set into a sorted concept sequence based on frequency of occurrence, select a root node C1 that has maximum frequency of occurrence, and sequentially add each concept from the sorted concept sequence to the root node C1 in the concept hierarchy based on a maximal similarity measure between a selected concept from the sorted concept sequence and the root node C1 in the concept hierarchy, or to another existing node Ci in the concept hierarchy based on a maximal similarity measure between a selected concept in the sorted concept sequence and that other existing node Ci in the concept hierarchy. 12. The method of claim 1, wherein constructing the concept hierarchy comprises: generating, by the system, a first sequence over a set of abstract concepts C1, . . . , Ck by simulating a random walk on a first hierarchical structure defined by a first branching factor and specified depth; generating, by the system, a second sequence over a set of regular concepts DI, . . . , Dk, said sequence extracted from a corpus; generating or retrieving, by the system, a vector representation for each of the concepts in the first sequence of abstract concepts and the second sequence of regular concepts; and identifying, by the system, one or more pairs of regular concepts to approximate corresponding pairs of abstract concepts based on analogies of relationships between the abstract concepts and the regular concepts. 13. The method of claim 1, further comprising: iteratively selecting, by the system, a concept from the first concept set; identifying, by the system, an associated neighborhood for each selected concept in the first concept set using iterative clustering and probability flow-based traversals to identify, for each concept in the first concept set, an associated neighborhood and corresponding strength measure; and creating, by the system, a hierarchy of associated neighborhoods, each of which comprises a representative concept to enable a human user to easily identify the neighborhood. 14. The method of claim 1, where the similarity measure is defined on a selected subset of dimensions of the concept vectors with uniform or non-uniform weights, where the selected dimensions and their weights can be modified in each iterative step of hierarchy construction. 15. 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 hierarchies, 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 concepts extracted from one or more content sources; receiving, by the system, a user request to produce a hierarchy of concepts from the first concept set using one or more specified hierarchy parameters; generating or retrieving, by the system, a vector representation of each of the concepts in the first concept set; performing, by the system, a natural language processing (NLP) analysis comparison of the vector representation of each of the concepts in the first concept set to determine a similarity measure for each pair of distinct concepts Ci and Cj in the first concept set; constructing, by the system, a concept hierarchy based on the one or more specified hierarchy parameters and the similarity measure for each pair of distinct concepts Ci and Cj in the first concept set; and displaying, by the system, the concept hierarchy to visually present a hierarchical structure conveying concept grouping of concepts from the first concept set to enable user navigation over the first concept set. 16. The information handling system of claim 15, wherein the set of instructions are executable to construct the concept hierarchy using a bottom-up method to iteratively build a concept graph by selecting distinct, unconnected concepts Ci, Cj from the first concept set based on a maximal similarity measure and identifying a first concept as a hierarchy root which has a maximal number of occurrences in the first concept set. 17. The information handling system of claim 15, wherein the set of instructions are executable to construct the concept hierarchy using a top-down/frequency method to sort the one or more concepts in the first concept set into a sorted concept sequence based on frequency of occurrence, select a root node C1 that has maximum frequency of occurrence, and sequentially add each concept from the sorted concept sequence to the root node C1 in the concept hierarchy based on a maximal similarity measure between a selected concept from the sorted concept sequence and the root node C1 in the concept hierarchy, or to another existing node Ci in the concept hierarchy based on a maximal similarity measure between a selected concept in the sorted concept sequence and that other existing node Ci in the concept hierarchy. 18. The information handling system of claim 15, wherein the set of instructions are executable to construct the concept hierarchy by: generating a first sequence over a set of abstract concepts C1, . . . , Ck by simulating a random walk on a first hierarchical structure defined by a first branching factor and specified depth; generating a second sequence over a set of regular concepts Dl, . . . , Dk, said sequence extracted from a corpus; generating a vector representation for each of the concepts in the first sequence of abstract concepts and the second sequence of regular concepts; and identifying one or more pairs of regular concepts to approximate corresponding pairs of abstract concepts based on analogies of relationships between the abstract concepts and the regular concepts. 19. The information handling system of claim 15, wherein the set of instructions are executable to perform actions of: iteratively selecting, by the system, a concept from the first concept set; identifying, by the system, an associated neighborhood for each selected concept in the first concept set using iterative clustering and probability flow-based traversals to identify, for each concept in the first concept set, an associated neighborhood and corresponding strength measure; and creating, by the system, a hierarchy of associated neighborhoods, each of which comprises a representative concept to enable a human user to easily identify the neighborhood. 20. The information handling system of claim 15, where the similarity measure is defined on a selected subset of dimensions of the concept vectors with uniform or non-uniform weights, where the selected dimensions and their weights can be modified in each iterative step of hierarchy construction. 21. 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 hierarchies by performing actions comprising: generating, by the system, at least a first concept set comprising one or more concepts extracted from one or more content sources; receiving, by the system, a user request to produce a hierarchy of concepts from the first concept set using one or more specified hierarchy parameters; generating or retrieving, by the system, a vector representation of each of the concepts in the first concept set; performing, by the system, a natural language processing (NLP) analysis comparison of the vector representation of each of the concepts in the first concept set to determine a similarity measure for each pair of distinct concepts Ci and Cj in the first concept set; constructing, by the system, a concept hierarchy based on the one or more specified hierarchy parameters and the similarity measure for each pair of distinct concepts Ci and Cj in the first concept set; and displaying, by the system, the concept hierarchy to visually present a hierarchical structure conveying concept grouping of concepts from the first concept set to enable user navigation over the first concept set. 22. The computer program product of claim 21, wherein constructing the concept hierarchy comprises using a bottom-up method to iteratively build a concept graph by selecting distinct, unconnected concepts Ci, Cj from the first concept set based on a maximal similarity measure and identifying a first concept as a hierarchy root which has a maximal number of occurrences in the first concept set. 23. The computer program product of claim 21, wherein constructing the concept hierarchy comprises using a top-down/frequency method to sort the one or more concepts in the first concept set into a sorted concept sequence based on frequency of occurrence, select a root node C1 that has maximum frequency of occurrence, and sequentially add each concept from the sorted concept sequence to the root node C1 in the concept hierarchy based on a maximal similarity measure between a selected concept from the sorted concept sequence and the root node C1 in the concept hierarchy, or to another existing node Ci in the concept hierarchy based on a maximal similarity measure between a selected concept in the sorted concept sequence and that other existing node Ci in the concept hierarchy. 24. The computer program product of claim 21, further comprising computer instructions that, when executed by the information handling system, causes the system to perform actions comprising: generating, by the system, a first sequence over a set of abstract concepts C1, . . . , Ck by simulating a random walk on a first hierarchical structure defined by a first branching factor and specified depth; generating, by the system, a second sequence over a set of regular concepts D1, . . . , Dk, said sequence extracted from a corpus; generating or retrieving, by the system, a vector representation for each of the concepts in the first sequence of abstract concepts and the second sequence of regular concepts; and identifying, by the system, one or more pairs of regular concepts to approximate corresponding pairs of abstract concepts based on analogies of relationships between the abstract concepts and the regular concepts. 25. The computer program product of claim 21, further comprising computer instructions that, when executed by the information handling system, causes the system to perform actions comprising: iteratively selecting, by the system, a concept from the first concept set; identifying, by the system, an associated neighborhood for each selected concept in the first concept set using iterative clustering and probability flow-based traversals to identify, for each concept in the first concept set, an associated neighborhood and corresponding strength measure; and creating, by the system, a hierarchy of associated neighborhoods, each of which comprises a representative concept to enable a human user to easily identify the neighborhood. 26. The computer program product of claim 21, where the similarity measure is defined on a selected subset of dimensions of the concept vectors with uniform or non-uniform weights, where the selected dimensions and their weights can be modified in each iterative step of hierarchy construction.