Statistical properties of probabilistic context-free grammars
Computational Linguistics
Scaling textual inference to the web
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Open information extraction from the web
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Markov Logic: An Interface Layer for Artificial Intelligence
Markov Logic: An Interface Layer for Artificial Intelligence
Unsupervised ontology induction from text
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Learning first-order Horn clauses from web text
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Open information extraction: the second generation
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
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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.