The development of knowledge base creation systems has mainly focused on information extraction without considering how to effectively reason over their databases of facts. One reason for this is that the inference required to learn a probabilistic knowledge base from text at any realistic scale is intractable. In this paper, we propose formulating the joint problem of fact extraction and probabilistic model learning in terms of Tractable Markov Logic (TML), a subset of Markov logic in which inference is low-order polynomial in the size of the knowledge base. Using TML, we can tractably extract new information from text while simultaneously learning a probabilistic knowledge base. We will also describe a testbed for our proposal: creating a biomedical knowledge base and making it available for querying on the Web. This is joint work with Pedro Domingos.
Chloe Kiddon is a fifth year graduate student at the University of Washington in Computer Science & Engineering. She works on machine learning, natural language understanding, and knowledge base construction. She received her B.S. with honors in Computer Science from Stanford University in 2008. She has been an NSF Graduate Fellow since 2010.