Markov Logic Sets: Towards Lifted Information Retrieval Using PageRank and Label Propagation

Inspired by “GoogleTM Sets” and Bayesian sets, we consider the problem of retrieving complex objects and relations among them, i.e., ground atoms from a logical concept, given a query consisting of a few atoms from that concept. We formulate this as a within-network relational learning problem using few labels only and describe an algorithm that ranks atoms using a score based on random walks with restart (RWR): the probability that a random surfer hits an atom starting from the query atoms. Specifically, we compute an initial ranking using personalized PageRank. Then, we find paths of atoms that are connected via their arguments, variablize the ground atoms in each path, in order to create features for the query. These features are used to re-personalize the original RWR and to finally compute the set completion, based on Label Propagation. Moreover, we exploit that RWR techniques can naturally be lifted and show that lifted inference for label propagation is possible. We eval...

• Type: Journal
• Year: 2011
• Where: AAAI
• Authors: Marion Neumann, Babak Ahmadi, Kristian Kersting
• Keywords: AAAI 2011, Intelligent Agents, Label Propagation, Logical Concept, Random Walks