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    1. Mentioned In 6 Articles

    2. Learning Markov Logic Network Structure via Hypergraph Lifting

      Stanley Kok koks@cs.washington.edu Pedro Domingos pedrod@cs.washington.edu Department of Computer Science & Engineering, University of Washington, Seattle, WA 98195, USA Abstract Markov logic networks (MLNs) combine logic and probability by attaching weights to first-order clauses, and viewing these as templates for features of Markov networks. Learning MLN structure from a relational database involves learning the clauses and weigh
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    3. Joint Unsupervised Coreference Resolution with Markov Logic

      Hoifung Poon Pedro Domingos Department of Computer Science and Engineering University of Washington Seattle, WA 98195-2350, U.S.A. {hoifung,pedrod}@cs.washington.edu Abstract Machine learning approaches to coreference resolution are typically supervised, and require expensive labeled data. Some unsupervised approaches have been proposed (e.g., Haghighi and Klein (2007)), but they are less accurate. In this paper, we present the first un
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    4. A General Method for Reducing the Complexity of RelationalInference and its Application to MCMC

      A General Method for Reducing the Complexity of Relational Inference And its Application to MCMC Hoifung Poon Pedro Domingos Marc Sumner Department of Computer Science and Engineering University of Washington Seattle, WA 98195-2350, U.S.A. {hoifung, pedrod, marcs}@cs.washington.edu Abstract Many real-world problems are characterized by complex relational structure, which can be succinctly represented in firstorder logic. However, many relational inference algorithms proceed by first fully instanti
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    5. Statistical Predicate Invention

      Stanley Kok koks@cs.washington.edu Pedro Domingos pedrod@cs.washington.edu Department of Computer Science & Engineering, University of Washington, Seattle, WA 98195, USA Abstract We propose statistical predicate invention as a key problem for statistical relational learning. SPI is the problem of discovering new concepts, properties and relations in structured data, and generalizes hidden variable discovery in statistical models and predicate invention in ILP. We
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    6. Joint Inference in Information Extraction

      Hoifung Poon Pedro Domingos Department of Computer Science and Engineering University of Washington Seattle, WA 98195-2350, U.S.A. {hoifung, pedrod}@cs.washington.edu Abstract The goal of information extraction is to extract database records from text or semi-structured sources. Traditionally, information extraction proceeds by first segmenting each candidate record separately, and then merging records that refer to the same entities. While computational
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    7. Markov Logic in Infinite Domains

      Markov Logic in Infinite Domains Parag Singla Pedro Domingos Department of Computer Science and Engineering University of Washington Seattle, WA 98195-2350, U.S.A. {parag, pedrod}@cs.washington.edu Abstract Combining first-order logic and probability has long been a goal of AI. Markov logic (Richardson & Domingos, 2006) accomplishes this by attaching weights to first-order formulas and viewing them as templates for features of Markov networks. Unfortunately, it does not have ...
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