Engineering SLS Algorithms for Statistical Relational Models

We present high performing SLS algorithms for learning and inference in Markov Logic Networks (MLNs). MLNs are a state-of-the-art representation formalism that integrates first-order logic and probability. Learning MLNs structure is hard due to the combinatorial space of candidates caused by the expressive power of first-order logic. We present current work on the development of algorithms for learning MLNs, based on the Iterated Local Search (ILS) metaheuristic. Experiments in real-world domains show that the proposed approach improves accuracy and learning time over the existing state-of-the-art algorithms. Moreover, MAP and conditional inference in MLNs are hard computational tasks too. This paper presents two algorithms for these tasks based on the Iterated Robust Tabu Search (IRoTS) schema. The first algorithm performs MAP inference by performing a RoTS search within a ILS iteration. Extensive experiments show that it improves over the state-of the-art algorithm in terms of solution quality and inference times. The second algorithm combines IRoTS with simulated annealing for conditional inference and we show through experiments that it is faster than the current state-of-the-art algorithm maintaining the same inference quality.