The Alchemy System for Statistical Relational AI: User Manual
Stanley Kok, Parag Singla, Matthew Richardson, Pedro Domingos, Marc Sumner Hoifung Poon · 2007
The Alchemy package provides a series of algorithms for statistical relational learning and probabilistic logic inference, based on the Markov logic representation. If you are not already familiar with Markov logic, we recommend that you read the papers Markov Logic Networks [7], Discriminative Training of Markov Logic Networks [9], Learning the Structure of Markov Logic Networks [3], Memory-Efficient Inference in Relational Domains [10] and Sound and Efficient Inference with Probabilistic and Deterministic Dependencies [6] (mln.pdf, dtmln.pdf, lsmln.pdf, lazysat.pdf and mcsat.pdf in the papers/ directory) before reading this manual. We welcome your feedback on any aspect of the Alchemy package. Please email us at [email protected] to let us know what you find easy or hard to use, what results you have obtained with Alchemy, the features you wish to have but are not currently provided, and any bugs that you encounter. Please cite Kok et al. (2005) [4] if you use the Alchemy system.