The KB we consider is a large triple store, which can be represented as a labeled, directed graph in which each entity a is a node, each binary relation R(a, b) is an edge labeled R between a and b, and unary concepts C(a) are represented as an edge labeled "isa" between the node for the entity a and a node for the concept C. We present a trainable inference method that learns to infer relations by combining the results of different random walks through this graph, and show that the method achieves good scaling properties and robust inference in a KB containing over 500,000 triples extracted from the web by the NELL system (Carlson et al., 2010).
we maximize the objective function where λ 1 controls L 1 -regularization to help structure selection, and λ 2 controls L 2 -regularization to prevent overfitting.