﻿ Markov逻辑网研究综述

# Markov逻辑网研究综述Survey of Markov Logic Networks

Markov逻辑网是将Markov网络与一阶谓词逻辑相结合的统计关系学习模型。Markov逻辑网在实体识别、数据融合、信息抽取等领域都有重要研究价值，具有广泛的应用。本文较为全面的介绍了Markov逻辑网的理论模型、推理、参数学习、与其他算法的比较，最后探讨Markov逻辑网未来的研究方向。

Abstract: Markov logic networks (MLNs) is a kind of statistical relational learning model which combines Markov network and first-order logic together. MLNs has the significant research value and in many areas it has widely applications, such as entity recognition, data integration and information extrac-tion. In this paper, we introduced the theoretical model of Markov logic networks, inference and pa-rametric learning of it and compared it with other. In the end, we discussed future works of MLNs.

[1] Liu, D.Y., Yu, P., Gao, Y., Qi, H. and Sun, S.Y. (2008) Research progress in statistical relational learning. Journal of Computer Research and Development, 45, 2110-2119.

[2] Koller, D. and Friedman, N. (2009) Probabilistic graphical models: Principles and techniques. The MIT Press, Cambridge.

[3] Jensen, F.V. and Nielsen, T.D. (2007) Bayesian Networks and Decision Graphs. 2nd Edition, Springer-Verlag, New York.

[4] Rabiner, L.R. (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77, 257-286.

[5] Arbib, M.A. (2003) The Handbook of Brain Theory and Neural Networks. MIT Press, Bos-ton.

[6] Richardson, M. and Domingos, P. (2004) Markov Logic Networks. Department of Computer Science and Engineering, University of Washington, Seattle.

[7] Wong, T.-L. (2014) Learning Markov logic networks with limited number of labeled training examples. International Journal of Knowledge-Based and Intelligent Engineering Systems, 2, 91-98.

[8] 耿素云, 屈婉玲 (1998) 离散数学. 高等教育出版社, 北京.

[9] Genesereth, M.R. and NiIsson, N.J. (1987) Logical foundations of artificial intelligence. Morgan Kaufmann, San Mateo.

[10] Gilks, W.R., Richardson, S. and Spiegelhalter, D.J. (1996) Markov chain Monte Carlo in practice. Chapman and Hall, London.

[11] Liu, Z.Y., Chen, D., Wurm, K.M. and von Wichert, G. (2015) Table-top scene analysis using knowledge-supervised MCMC. Robotics and Computer-Integrated Manufacturing, 33, 110-123.

[12] Riedel, S. (2008) Improving the accuracy and efficiency of map inference for Markov logic. Proceedings of the Annual Conference on Uncertainty in Artificial Intelligence, Helsinki, 9-12 July 2008, 468-475.

[13] Poon, H. and Domingos, P. (2006) Sound and efficient inference with probabilistic and deterministic dependencies. Proceedings of the 2lst National Conference on Artificial Intelligence (AAAI 2006), Boston, 16-20 July 2006, 458-463.

[14] Liu, D.C. and Nocedal, J. (1989) On the limited memory BFGS method for large scale optimization. Mathematical Programming, 45, 503-528.

[15] 徐从富, 郝春亮, 苏保君, 楼俊杰 (2011) 马尔科夫逻辑网研究. 软件学报, 8, 1699-1713.

[16] Richardson, M. and Domingos, P. (2006) Markov logic networks. Machine Learning, 62, 107-136.

[17] Singla, P. and Domingos, P. (2006) Memory efficient inference in relational domains. Proceedings of the 21st National Conference on Artificial Intelligence (AAAI 2006), Boston, 16-20 July 2006, 488-493.

[18] Gogate, V., Webb, W.A. and Domingos, P. (2010) Learning efficient Markov networks. Proceedings of the 24th Annual Conference on Neural Information Processing Systems (NIPS-2010), Vancouver, 6-9 December 2010, 748-756.

[19] Singla, P. and Domingos, P. (2005) Discriminative training of Markov logic networks. Proceedings of the 20th National Conference on Artificial Intelligence (AAAI 2005), Pittsburgh, 9-13 July 2005, 868-873.

[20] Ngo, L. and Haddawy, P. (1997) Answering queries from context-sensitive probabilistic knowledge bases. Theoretical Computer Science, 171, 147-177.

[21] Muggleton, S. (1996) Stochastic logic programs. Proceedings of the 5th International Workshop on Inductive Logic Programming, IOS Press, Amsterdam, 1996, 254-264.

[22] Sato, T. and Kamcya, Y. (1997) PRJSM: A language for symbolic-statistical modeling. Proceedings of the 15th International Joint Conference on Artificial Intelligence, Nagoya, 23-29 August 1997, 1330-1339.

[23] 孙舒杨, 刘大有, 孙成敏, 黄冠利 (2007) 统计关系学习模型Markov 逻辑网综述. 计算机应用研究, 2, 1-3.

[24] Domingos, P. and Lowd, D. (2009) Markov logic: An interface layer for artificial intelligence. Morgan and Claypool, San Rafael.

[25] Kok, S., Singla, P. and Richardson, M. (2005) The alchemy system for statistical relational AI: User manual. Department of Computer Science and Engineering, VSi University of Washington, Seattle.

[26] Singla, P. and Domingos, P. (2006) Entity resolution with Markov logic. Proceedings of the 6th IEEE Industrial Conference on Data Mining (ICDM), Hong Kong, 18-22 December 2006, 572-582.

[27] Paolo, F., Francesco, G., Marco, L. and Simone, M. (2014) Markov logic networks for optical chemical structure recognition. Journal of Chemical Information and Modeling, 8, 2380-2390.

[28] 胡宜敏, 宋良图, 陈鹏, 魏圆圆, 宋雅茹 (2013) 一种基于Markov逻辑网的中文地理名称实体解析方法. 模式识别与人工智能, 1, 114-122.

[29] Yang, J.M., Cai, Y., Wang, Y., Zhu, J., Zhang, L. and Ma, W.Y. (2009) Incorporating site-level knowledge to extract structured data from web forums. Proceedings of the 18th International Conference on World Wide Web (WWW), Madrid, Spain, 20-24 April 2009, 181-190.

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

[31] 刘小军, 邢永康, 袁文群, 武南南 (2013) 马尔可夫逻辑网在信息抽取中的应用. 世界科技研究与发展, 4, 465- 468.

[32] 刘永彬, 杨炳儒, 李广源, 刘英华 (2012) 基于马尔可夫逻辑网的联合推理开放信息抽取. 计算机科学, 9, 202- 205.

[33] Zhu, J., Nie, Z.P., Liu, X.J., Zhang, B. and Wen, J.-R. (2009) StatSnowball: A statistical approach to extracting entity relationships. Proceedings of the 18th ACM International World Wide Web Conference, Madrid, 20-24 April 2009, 101-110.

[34] Singla, P., Kautz, H., Luo, J.B. and Gallagher, A. (2008) Discovery of social relationships in consumer photo collections using Markov logic. Proceedings of the CVPR Workshop on Semantic Learning and Applications in Multimedia, Anchorage, 24-26 June 2008, 1-7.

[35] Poon, H. and Domingos, P. (2009) Unsupervised semantic parsing. Proceedings of the 2009 International Conference on Empirical Methods in Natural Language Processing (EMNLP), Singapore, 6-7 August 2009, 1-10.

[36] 杨博, 蔡东风, 赵奇猛, 杨华 (2014) 融合WordNet的无监督语义分析研究. 小型微型计算机系统, 2, 368-373.

[37] 杨立公, 汤世平, 朱俭 (2013) 基于马尔科夫逻辑网的句子情感分析方法. 北京理工大学学报, 6, 600-604.

[38] McNeill, F., Halpin, H., Klein, E. and Bundy, A. (2006) Merging stories with shallow semantics. Proceedings of the Workshop on Knowledge and Reasoning for Language Processing (KRAQ 2006), Trento, 3-7 April 2006, 37-42.

[39] Wong, T.L., Chow, K.O., Wang, F.L. and Tsang, P.M. (2010) Improving Markov logic network learning using unlabeled data. Proceedings of the 2010 International Conference on Machine Learning and Cybernetics (ICMLC), Qingdao, 11-14 July 2010, 236-240.

[40] 李燕 (2013) 基于马尔可夫转移矩阵的多步过程挖掘方法. 信息系统工程, 2, 37-40.

[41] 王星, 方滨兴, 张宏莉, 何慧, 赵蕾 (2013) 关系分类的学习界限研究. 软件学报, 11, 2508-2521.

[42] Davis, J. and Domingos, P. (2009) Deep transfer via second-order Markov logic. Proceedings of the 20th International Conference on Machine Learning (ICML), Montreal, 14-18 June 2009, 217-224.

[43] Gayathri, K.S., Elias, S. and Ravindran, B. (2015) Hierarchical activity recognition for dementia care using Markov logic network. Personal and Ubiquitous Computing, 19, 271-285.

[44] Cheng, V. and Li, C.H. (2007) Topic detection via participation using Markov logic network. Proceedings of the Third International IEEE Conference on Signal-Image Technologies and Internet-Based System (SITIS), Shanghai, 16-18 December 2007, 85-91.

[45] Chahuara, P., Portet, F. and Vacher, M. (2013) Making context aware decision from uncertain information in a smart home: A Markov logic network approach. Proceedings of the 4th International Joint Conference, Dublin, 3-5 December 2013, 78-93.

[46] 张玉芳, 黄涛, 艾冬梅, 熊忠阳 (2009) Markov逻辑网及其在文本分类中的应用. 计算机应用, 10, 2729-2732.

[47] 张玉芳, 孔润, 田源, 熊忠阳 (2011) 基于Markov逻辑网的超文本分类. 南京大学学报(自然科学版), 5, 571-577.

[48] 张玉芳, 黄涛, 艾冬梅, 熊忠阳, 唐容君 (2010) Markov逻辑网在重复数据删除中的应用. 重庆大学学报, 8, 36- 41.

[49] 吴蕾, 张文生, 王珏 (2014) 异构信息网络数据上的融合概率图模型. 计算机科学与探索, 6, 712-718.

[50] 吴蕾, 张文生, 王珏 (2015) 基于深度学习框架的隐藏主题变量图模型. 计算机研究与发展, 1, 191-199.

[51] 张永新, 李庆忠, 彭朝晖 (2012) 基于Markov逻辑网的两阶段数据冲突解决方法. 计算机学报, 1, 101-111.

Top