Alchemy: Open Source AI. Welcome to the Alchemy system! Alchemy is a software package providing a series of algorithms for statistical relational learning and probabilistic logic inference, based on the Markov logic representation. Alchemy allows you to easily develop a wide range of AI applications, including: Collective classification; Link prediction; Entity resolution; Social network modeling; Information extraction.
Keywords for this software
References in zbMATH (referenced in 9 articles )
Showing results 1 to 9 of 9.
- Ibrahim, Mohamed-Hamza; Pal, Christopher; Pesant, Gilles: Improving probabilistic inference in graphical models with determinism and cycles (2017)
- Domingos, Pedro; Lowd, Daniel; Kok, Stanley; Nath, Aniruddh; Poon, Hoifung; Richardson, Matthew; Singla, Parag: Unifying logical and statistical AI (2016)
- Schulte, Oliver; Qian, Zhensong; Kirkpatrick, Arthur E.; Yin, Xiaoqian; Sun, Yan: Fast learning of relational dependency networks (2016)
- Van Haaren, Jan; Van den Broeck, Guy; Meert, Wannes; Davis, Jesse: Lifted generative learning of Markov logic networks (2016)
- Khot, Tushar; Natarajan, Sriraam; Kersting, Kristian; Shavlik, Jude: Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases (2015)
- Mangal, Ravi; Zhang, Xin; Nori, Aditya V.; Naik, Mayur: Volt: a lazy grounding framework for solving very large maxsat instances (2015)
- Skarlatidis, Anastasios; Paliouras, Georgios; Artikis, Alexander; Vouros, George A.: Probabilistic event calculus for event recognition (2015)
- Finthammer, Marc; Thimm, Matthias: An integrated development environment for probabilistic relational reasoning (2012)
- Domingos, Pedro: Toward knowledge-rich data mining (2007) ioport
Further publications can be found at: http://alchemy.cs.washington.edu/papers/