AAAI Publications, Eighth Symposium on Abstraction, Reformulation, and Approximation

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Ontology-Based Link Prediction in the LiveJournal Social Network
Doina Caragea, Vikas Bahirwani, Waleed Aljandal, William H. Hsu

Last modified: 2009-10-22

Abstract


LiveJournal is a social network journal service with focus on user interactions. As for many other online social networks, predicting potential friendships in the LiveJournal network is a problem of great practical interest. Previous work has shown that graph features extracted from the graph associated with the network are good predictors for friendship links. However, contrary to the intuition, user data (e.g., interests shared by two users) does not always improve the predictions obtained with graph features alone. This could be due to the fact that features constructed from a large number of user declared interests cannot capture the implicit semantic of the interests. To test this hypothesis, we use a clustering approach to build an interest ontology, and explore the ability of the ontology to improve the performance of learning algorithms at predicting friendship links, when interest-based features are used alone or in combination with graph-based features. The results show that ontology-based features can help improve the performance of several machine learning classifiers (in particular, random forest classifiers) at the task of predicting links in the LiveJournal social network.

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