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KeEL: knowledge enhanced entity linking in automatic biography construction

The Journal of China Universities of Posts and Telecommunications 2015年第1期22卷 57-64,71页

作者： Zhang Tianlei Zhang Xinyu Guo Mu Computer Science Department Tsinghua University

Biography is a direct and extensive way to know the representation of well known peoples, however, for common people, there is poor knowledge for them to be recognized. In recent years, information extraction （IE） technologies have been used to automatically generate biography for any people with online information. One of the key challenges is the entity linking （EL） which can link biography sentence to corresponding entities. Currently the used general EL systems usually generate errors originated from entity name variation and ambiguity. Compared with general text, biography sentences possess unique yet rarely studied relational knowledge （RK） and temporal knowledge （TK）, which could sufficiently distinguish entities. This article proposed a new statistical framework called the knowledge enhanced EL （KeEL） system for automated biography construction. It utilizes commonsense knowledge like PK and TK to enhance Entity Linking. The performance of KeEL on Wikipedia data was evaluated. It is shown that, compared with state-of-the-art method, KeEL significantly improves the precision and recall of Entity Linking.

关键词： knowledge enhanced entity linking entity linking biography construction markov logic network Knowledge

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Research on Action Recognition and Content Analysis in Videos Based on DNN and MLN

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Computers, Materials & Continua 2019年第9期61卷 1189-1204页

作者： Wei Song Jing Yu Xiaobing Zhao Antai Wang School of Information Engineering Minzu University of ChinaBeijing100081China National language resource monitoring&Research Center Minority Languages Branch Minzu University of ChinaBeijing100081China School of Electronic Information Engineering Beijing Jiaotong UniversityBeijing100044China New Jersey Institute of Technology 323 Dr Martin Luther King Jr Blvd.NewarkNJ07102USA

In the current era of multimedia information,it is increasingly urgent to realize intelligent video action recognition and content *** the past few years,video action recognition,as an important direction in computer vision,has attracted many researchers and made much ***,this paper reviews the latest video action recognition methods based on Deep Neural network and markov logic ***,we analyze the characteristics of each method and the performance from the experiment *** compare the emphases of these methods and discuss the application ***,we consider and prospect the development trend and direction of this field.

关键词： Video action recognition deep learning network markov logic network

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An Entity Answer Ranking Method Based on MLNs

An Entity Answer Ranking Method Based on MLNs

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2013年中国智能自动化学术会议

作者： Fangqiong Chen Zhengtao Yu Jianyi Guo Tao Shen Yantuan Xian The School of Information Engineering and Automation Kunming University of Science and Technology The Intelligent Information Processing Key Laboratory Kunming University of Science and Technology

For the characteristics of factoid and list answers in domain Q&A system,we built a ranking model combined with multiple features of domain entity answers based on *** method uses predicate formulas to describe the relevant features of the questions-candidate answers and the answersknowledge base,merging these features into markov logic network,and then adopting discriminant training learning algorithm to learn the weights of feature parameters,which can give different weights according to the relevance of different features,finally it use MC-SAT algorithm reasoning to get the relevance of questions and answers,realize the answer *** show that the proposed method can greatly improve the answer precision and recall rates compared with other methods.

关键词： Question-answering system markov logic network Learning to rank Multiple features

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