Disclosed in some examples are systems, methods, and machine readable mediums which allow for the automatic creation of a skills hierarchy. The skills hierarchy comprises an organization of a standardized list of skills into a hierarchy that describes category relationships between the skills in the hierarchy. The category relationships may include no relationships, parent relationships, and child relationships. A skill may be considered a parent of another skill if the parent skill describes a broader category of skill that includes the child. Other relationships such as grandparent (e.g., a parent's parent), great-grandparent, grandchild, great grandchild and so on may be defined inferentially as well. In some examples, the constructed hierarchy may be organized with broader skills at higher levels and narrower skills at lower levels.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for creating a hierarchy of topics, the method comprising: training a topic classifier using training data, the training data comprising a plurality of trained topic pairs, each topic in the plurality of trained topic pairs selected from a predetermined list of topics; determining for each particular topic pair in a target set of topic pairs, using the topic classifier, a probability that the first topic in each particular topic pair is a parent of the second topic in each particular topic pair, the first and second topics included in the predetermined list of topics; collecting, in response to the determining the probability for each particular topic pair, a collection of probabilities for the target set of topic pairs; constructing a hierarchy of topics based upon the collection of probabilities for the target set of topic pairs. 2. The method of claim 1, wherein the topics are skills. 3. The method of claim 2, wherein the topic classifier is a random forest classifier. 4. The method of claim 2, wherein the topic classifier takes as input feature data describing relationships between two or more topics and training the topic classifier comprises training the topic classifier using the training data and the feature data. 5. The method of claim 2, wherein the topic classifier utilizes feature data in determining the probability that for each particular topic pair in the target set, the first topic in the particular topic pair is a parent of the second topic in the particular topic pair. 6. The method of claim 5, wherein the feature data includes data on a probability of co-occurrence in member profiles of a social networking service between the first and second topics. 7. The method of claim 6, wherein the feature data includes a closeness centrality metric based upon the probability of co-occurrence between the first and second topics. 8. The method of claim 5, wherein the feature data includes a pre-existing hierarchy describing a parent-child relationship between the first and second topics. 9. The method of claim 5, wherein the feature data includes a ratio of a number of times the first topic was explicitly included in member profiles of a social networking service to a number of times the first topic was implicitly included in member profiles of the social networking service. 10. A machine readable medium for creating a hierarchy of topics, the machine readable medium comprising instructions, which when performed by the machine, causes the machine to perform the operations comprising: training a topic classifier using training data, the training data comprising a plurality of trained topic pairs, each topic in the plurality of trained topic pairs selected from a predetermined list of topics; determining for each particular topic pair in a target set of topic pairs, using the topic classifier, a probability that the first topic in each particular topic pair is a parent of the second topic in each particular topic pair, the first and second topics included in the predetermined list of topics; collecting, in response to the determining the probability for each particular topic pair, a collection of probabilities for the target set of topic pairs; constructing a hierarchy of topics based upon the collection of probabilities for the target set of topic pairs. 11. The machine-readable medium of claim 10, wherein the topics are skills. 12. The machine-readable medium of claim 11, wherein the topic classifier is a random forest classifier. 13. The machine-readable medium of claim 11, wherein the topic classifier takes as input feature data describing relationships between two or more topics and the operations for training the topic classifier comprises training the topic classifier using the training data and the feature data. 14. The machine readable medium of claim 11, wherein the topic classifier utilizes feature data in determining the probability that for each particular topic pair in the target set, the first topic in the particular topic pair is a parent of the second topic in the particular topic pair. 15. A system for creating a hierarchy of topics, the system comprising: a training module configured to: train a topic classifier using training data, the training data comprising a plurality of trained topic pairs, each topic in the plurality of trained topic pairs selected from a predetermined list of topics; and a hierarchy generation module configured to: determining for each particular topic pair in a target set of topic pairs, using the topic classifier, a probability that the first topic in each particular topic pair is a parent of the second topic in each particular topic pair, the first and second topics included in the predetermined list of topics; collecting, in response to the determining the probability for each particular topic pair, a collection of probabilities for the target set of topic pairs; and constructing a hierarchy of topics based upon the collection of probabilities for the target set of topic pairs. 16. The system of claim 15, wherein the topics are skills. 17. The system of claim 15, wherein the topic classifier takes as input feature data describing relationships between two or more topics and training the topic classifier comprises training the topic classifier using the training data and the feature data. 18. The system of claim 15, wherein the topic classifier utilizes feature data in determining the probability that for each particular topic pair in the target set, the first topic in the particular topic pair is a parent of the second topic in the particular topic pair. 19. The system of claim 18, wherein the feature data includes data on a probability of co-occurrence in member profiles of a social networking service between the first and second topics. 20. The system of claim 19, wherein the feature data includes a closeness centrality metric based upon the probability of co-occurrence between the first and second topics.