Markov Logic Network and Its Applications in Information Extraction

doi:10.3969/j.issn.1000-3428.2012.18.044

Abstract

Abstract: This paper addresses the theoretical model, structure and parameter learning algorithms, two kinds of inference problems of Markov Logic Network(MLN) and its applications in Information Extraction(IE), including named entity recognition, entity relation extraction and entity resolution. MLN model can combine one-order predicate logic and probability graph model, fuse modular knowledge into Markov network, and describe complicate characteristics.

Key words: Markov Logic Network(MLN), weight learning, structure learning, inference, named entity recognition, entity relation extraction, entity resolution

摘要： 介绍Markov逻辑网的理论模型，阐述Markov逻辑网的结构和参数学习算法及2种基本类型的推理，从命名实体识别、实体关系抽取和实体解析3个方面总结Markov逻辑网在信息抽取中的应用现状。分析结果表明，Markov逻辑网模型能较好地将一阶谓词逻辑和概率图模型相结合，灵活地在Markov网中融入模块化知识，描述复杂的特征。

关键词: Markov逻辑网, 权重学习, 结构学习, 推理, 命名实体识别, 实体关系抽取, 实体解析

CLC Number:

TP391

TAN Yong-Xin, LUO Jun-Yong, YIN Mei-Juan. Markov Logic Network and Its Applications in Information Extraction[J]. Computer Engineering, 2012, 38(18): 162-165.

谭永兴, 罗军勇, 尹美娟. Markov逻辑网及其在信息抽取中的应用[J]. 计算机工程, 2012, 38(18): 162-165.

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