11. A data processor readable medium, said medium comprising program code stored therein, said medium not being a transitory signal, said program code configured to be executed by a processor of a data processing system to perform a method of facilitating interaction between users of an electronic community, said method comprising: reviewing a user activity log for each user in the electronic community, each user having an associated user profile stored in a user profile and relationship database; executing a natural language parser to extract noun phrases from the user activity log; updating the user profiles from the extracted noun phrases, a keyword being associated with each extracted noun phrase, said updating based on a usage frequency of the extracted noun phrases and an importance value of the keywords; storing the updated user profiles in the user profile and relationship database; executing a similarity based clustering algorithm to generate clusters of the updated user profiles, each cluster consisting of a group of users of the users in the electronic community, each cluster representing a relationship between the users in each group; and storing each cluster in the user profile and relationship database, wherein a digital hierarchical dictionary comprises synsets, each synset being a set of cognitive synonyms consisting of noun phrases, said synsets being interlinked into a semantic hierarchical tree within the digital hierarchical dictionary, wherein the keyword associated with each extracted noun phrase is in a synset within the semantic hierarchical tree, wherein the similarity based clustering algorithm comprises a member importance function and a member similarity function, wherein the member importance function ascertains an importance value of keywords as a depth of said keywords in the semantic hierarchical tree, wherein the member similarity function ascertains a similarity distance between keywords as a path distance between said keywords in the semantic hierarchical tree, and wherein said executing the similarity based clustering algorithm comprises: using the member importance function and the member similarity function to ascertain the clusters.
11. A data processor readable medium, said medium comprising program code stored therein, said medium not being a transitory signal, said program code configured to be executed by a processor of a data processing system to perform a method of facilitating interaction between users of an electronic community, said method comprising: reviewing a user activity log for each user in the electronic community, each user having an associated user profile stored in a user profile and relationship database; executing a natural language parser to extract noun phrases from the user activity log; updating the user profiles from the extracted noun phrases, a keyword being associated with each extracted noun phrase, said updating based on a usage frequency of the extracted noun phrases and an importance value of the keywords; storing the updated user profiles in the user profile and relationship database; executing a similarity based clustering algorithm to generate clusters of the updated user profiles, each cluster consisting of a group of users of the users in the electronic community, each cluster representing a relationship between the users in each group; and storing each cluster in the user profile and relationship database, wherein a digital hierarchical dictionary comprises synsets, each synset being a set of cognitive synonyms consisting of noun phrases, said synsets being interlinked into a semantic hierarchical tree within the digital hierarchical dictionary, wherein the keyword associated with each extracted noun phrase is in a synset within the semantic hierarchical tree, wherein the similarity based clustering algorithm comprises a member importance function and a member similarity function, wherein the member importance function ascertains an importance value of keywords as a depth of said keywords in the semantic hierarchical tree, wherein the member similarity function ascertains a similarity distance between keywords as a path distance between said keywords in the semantic hierarchical tree, and wherein said executing the similarity based clustering algorithm comprises: using the member importance function and the member similarity function to ascertain the clusters. 13. The medium of claim 11 , wherein the similarity based clustering algorithm uses a similarity threshold to constrain a total number of clusters returned from said executing the similarity based clustering algorithm, and wherein said executing the similarity based clustering algorithm comprises: increasing the similarity threshold to decrease the total number of clusters returned from said executing the similarity based clustering algorithm; or decreasing the similarity threshold to increase the total number of clusters returned from said executing the similarity based clustering algorithm.