System and method for interaction between users of an online community
View Patent ↗There is disclosed a method of facilitating interaction between users of an electronic community. In an embodiment, the method comprises: reviewing a user activity log for each user in the electronic community; executing a natural language parser to extract significant noun phrases from the user activity log; updating user profiles from the newly extracted noun phrases, based on their usage frequency and importance value; and storing the updated profiles in a user profile and relationship data base; and executing a similarity based clustering algorithm to cluster user profiles, thereby discovering relationships among users and storing them in a user profile and relationship database. The method may further comprise displaying for each user the one or more relationships to which the user is assigned, together with a list of users assigned to the one or more relationships. The method may also comprise storing for each user the relationship to which the user is assigned in a user profile and relationship database.
1. A method of facilitating interaction between users of an electronic community, 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 each 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.
2. The method of claim 1 , wherein the method further comprises calculating a relationship importance value of the relationship represented by a first cluster of the clusters of users by:
using the member importance function to ascertain importance values of the keywords of the first cluster; and
calculate the importance value of the relationship represented by the first cluster as a sum of the ascertained importance values of the keywords of the first cluster.
3. The method of claim 1 , 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.
4. The method of claim 1 , wherein said updating comprises:
using the member importance function to ascertain a first importance value as a first depth in the semantic hierarchical tree of a first keyword associated with a first noun phrase of the extracted noun phrases;
ascertaining, via use of the member importance function, a second importance value as a second depth in the semantic hierarchical tree of a second keyword whose depth in the semantic hierarchical tree exceeds the first depth and whose meaning is more specific and descriptive than is the meaning of the first keyword; and
in response to ascertaining that the second depth exceeds the first depth, replacing the first noun phrase in a first user profile of the user profiles by a second noun phrase to which the second keyword is associated.
5. The method of claim 1 , wherein a first noun phrase of the extracted noun phrases is a first name not found in the digital hierarchical dictionary, and wherein a dataset consisting of a text file or a database table comprises mappings of names to respective noun phrases, wherein the mappings comprise a first mapping of the first name to a respective second noun phrase, and wherein the method further comprises:
ascertaining the second noun phrase from the first mapping.
6. A data processing system comprising a processor, a memory coupled to the processor, and a computer readable storage device coupled to the processor, said storage device containing program code configured to be executed by the processor via the memory to implement 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.
7. The system of claim 6 , wherein the method further comprises calculating a relationship importance value of the relationship represented by a first cluster of the clusters of users by:
using the member importance function to ascertain importance values of the keywords of the first cluster; and
calculate the importance value of the relationship represented by the first cluster as a sum of the ascertained importance values of the keywords of the first cluster.
8. The system of claim 6 , 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.
9. The system of claim 6 , wherein said updating comprises:
using the member importance function to ascertain a first importance value as a first depth in the semantic hierarchical tree of a first keyword associated with a first noun phrase of the extracted noun phrases;
ascertaining, via use of the member importance function, a second importance value as a second depth in the semantic hierarchical tree of a second keyword whose depth in the semantic hierarchical tree exceeds the first depth and whose meaning is more specific and descriptive than is the meaning of the first keyword; and
in response to ascertaining that the second depth exceeds the first depth, replacing the first noun phrase in a first user profile of the user profiles by a second noun phrase to which the second keyword is associated.
10. The system of claim 6 , wherein a first noun phrase of the extracted noun phrases is a first name not found in the digital hierarchical dictionary, and wherein a dataset consisting of a text file or a database table comprises mappings of names to respective noun phrases, wherein the mappings comprise a first mapping of the first name to a respective second noun phrase, and wherein the method further comprises:
ascertaining the second noun phrase from the first mapping.
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.
12. The medium of claim 11 , wherein the method further comprises calculating a relationship importance value of the relationship represented by a first cluster of the clusters of users by:
using the member importance function to ascertain importance values of the keywords of the first cluster; and
calculate the importance value of the relationship represented by the first cluster as a sum of the ascertained importance values of the keywords of the first cluster.
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.
14. The medium of claim 11 , wherein said updating comprises:
using the member importance function to ascertain a first importance value as a first depth in the semantic hierarchical tree of a first keyword associated with a first noun phrase of the extracted noun phrases;
ascertaining, via use of the member importance function, a second importance value as a second depth in the semantic hierarchical tree of a second keyword whose depth in the semantic hierarchical tree exceeds the first depth and whose meaning is more specific and descriptive than is the meaning of the first keyword; and
in response to ascertaining that the second depth exceeds the first depth, replacing the first noun phrase in a first user profile of the user profiles by a second noun phrase to which the second keyword is associated.