公开(公告)号：US20130144812A1

公开(公告)日：2013-06-06

申请号：US13308571

申请日：2011-12-01

申请人： Arun Tejasvi Chaganty ,  Akash Lal ,  Aditya V. Nori ,  Sriram Rajamani

发明人： Arun Tejasvi Chaganty ,  Akash Lal ,  Aditya V. Nori ,  Sriram Rajamani

IPC分类号： G06F15/18

CPC分类号： G06N7/005

摘要： Various technologies described herein pertain to approximating an inputted probabilistic model for statistical relational learning. An initial approximation of formulae included in an inputted probabilistic model can be formed, where the initial approximation of the formulae omits axioms included in the inputted probabilistic model. Further, an approximated probabilistic model of the inputted probabilistic model can be constructed, where the approximated probabilistic model includes the initial approximation of the formulae. Moreover, the approximated probabilistic model and evidence can be fed to a relational learning engine, and a most probable explanation (MPE) world can be received from the relational learning engine. The evidence can comprise existing valuations of a subset of relations included in the inputted probabilistic model. The MPE world can include valuations for the relations included in the inputted probabilistic model. The MPE world can be outputted when the input probabilistic model lacks an axiom violated by the MPE world.

摘要翻译： 本文描述的各种技术涉及近似输入的用于统计关系学习的概率模型。 可以形成包括在输入的概率模型中的公式的初始近似，其中公式的初始近似省略包括在输入的概率模型中的公理。 此外，可以构造输入的概率模型的近似概率模型，其中近似概率模型包括公式的初始近似。 此外，近似的概率模型和证据可以被馈送到关系学习引擎，并且可以从关系学习引擎接收到最可能的解释（MPE）世界。 证据可以包括输入的概率模型中包括的关系子集的现有估值。 MPE世界可以包括输入概率模型中包含的关系的估值。 当输入概率模型缺少MPE世界违反的公理时，可以输出MPE世界。