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检索条件"机构=Key Labs. of Data Engineering and Knowledge Engineering"

共 41 条记录，以下是11-20 订阅

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Detecting event rumors on Sina Weibo automatically

Detecting event rumors on Sina Weibo automatically

引用

15th Asia-Pacific Web Conference on Web Technologies and Applications, APWeb 2013

作者： Sun, Shengyun Liu, Hongyan He, Jun Du, Xiaoyong Key Labs. of Data Engineering and Knowledge Engineering China School of Information Renmin University of China 100872 China Department of Management Science and Engineering Tsinghua University 100084 China

ISBN: (纸本)9783642374005

Sina Weibo has become one of the most popular social networks in China. In the meantime, it also becomes a good place to spread various spams. Unlike previous studies on detecting spams such as ads, pornographic messages and phishing, we focus on identifying event rumors (rumors about social events), which are more harmful than other kinds of spams especially in China. To detect event rumors from enormous posts, we studied the characteristics of event rumors and extracted features which can distinguish rumors from ordinary posts. The experiments conducted on real dataset show that the new features are effective to improve the rumor classifier. Further analysis of the event rumors reveals that they can be classified into 4 different types. We propose an approach for detecting one major type, text-picture unmatched event rumors. The experiment demonstrates that this approach is well-performed. © 2013 Springer-Verlag.

关键词： Social networking (online)

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MrCAR: A Multi-relational Classification Algorithm based on Association Rules

MrCAR: A Multi-relational Classification Algorithm based on ...

引用

International Conference on Web Information Systems and Mining

作者： Gu, Yingqin Liu, Hongyan He, Jun Hu, Bo Du, Xiaoyong Key Labs. of Data Engineering and Knowledge Engineering MOE China Information School Renmin University of China Beijing 100872 China School of Economics and Management Tsinghua University Beijing 100084 China

ISBN: (纸本)9780769538174

Classification is an important subject in data mining and machine learning, which has been studied extensively and has a wide range of applications. Classification based on association rules is one of the most effective classification method, whose accuracy is higher and discovered rules are easier to understand comparing with classical classification methods. However, current algorithms for classification based on association rules is single table oriented, which means they can only apply to the data stored in a single relational table. Directly applying these algorithms in multi-relational data environment will result in many problems. This paper proposes a novel algorithm MrCAR for classification based on association rules in multi-relational data environment. MrCAR mines relevant features in each table to predict the class label. Close itemsets technique and Tuple ID Propagation method are used to improve the performance of the algorithm. Experimental results show that MrCAR has higher accuracy and better understandability comparing with a typical existing multirelational classification algorithm.

关键词： Association rules

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Predicting retweet behavior in Weibo social network

Predicting retweet behavior in Weibo social network

引用

13th International Conference on Web Information Systems engineering, WISE 2012

作者： Zhang, Hongbo Zhao, Qun Liu, Hongyan Xiao, Ke He, Jun Du, Xiaoyong Chen, Hong Key Labs. of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China China School of Economics and Management Tsinghua University China

ISBN: (纸本)9783642350627

Retweeting ensures the information diffusion in micro-blog services. By this simple way, it is convenient for a user to share and spread interesting information in the whole network. In this paper, we consider many features to compute the probability that a user retweets a tweet. With the probability, we build a retweet model to predict the number of possible-views of a tweet. The model is based on the theory of random walks. Experiments conducted on real dataset show that the proposed method has a good performance than the traditional prediction methods. © 2012 Springer-Verlag.

关键词： Forecasting

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A fast two-stage algorithm for computing SimRank and its extensions

A fast two-stage algorithm for computing SimRank and its ext...

引用

1st International Workshop on Graph database, IWGD 2010, 1st International Workshop on Advanced Techniques on XML data Management, XMLDM 2010, 2nd International Workshop on WCMT 2010, Held in Conj. with WAIM 2010

作者： Jia, Xu Liu, Hongyan Zou, Li He, Jun Du, Xiaoyong Key Labs. of Data Engineering and Knowledge Engineering Ministry of Education China Department of Computer Science Renmin University of China 100872 China Department of Management Science and Engineering Tsinghua University 100084 China

ISBN: (纸本)3642167195

Similarity estimation can be used in many applications such as recommender system, cluster analysis, information retrieval and link prediction. SimRank is a famous algorithm to measure objects' similarities based on link structure. We observe that if one node has no in-link, similarity score between this node and any of the others is always zero. Based on this observation, we propose a new algorithm, fast two-stage SimRank (F2S-SimRank), which can avoid storing unnecessary zeros and can accelerate the computation without accuracy loss. Under the circumstance of no accuracy loss, this algorithm uses less computation time and occupies less main memory. Experiments conducted on real and synthetic datasets demonstrate the effectiveness and efficiency of our F2S-SimRank. © 2010 Springer-Verlag.

关键词： Cluster analysis

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An Adaptive Method for the Efficient Similarity Calculation

An Adaptive Method for the Efficient Similarity Calculation

引用

14th International Conference on database Systems for Advanced Applications

作者： Cai, Yuanzhe Liu, Hongyan He, Jun Du, Xiaoyong Jia, Xu Key Labs of Data Engineering and Knowledge Engineering MOE China School of Information Renmin University of China China Department of Management Science and Engineering Tsinghua University China

ISBN: (纸本)9783642008863

SimRank is a well-known algorithm for similarity calculation based on object-to-object relationship. However, it suffers from high computation cost. Inthis paper, we find that the convergence behavior of different object pairs is different when we use SimRank to compute the similarity of objects. Many similarity scores converge fast, while others need more time before convergence. Based on this observation, we propose an adaptive method called Adaptive-SimRank to speed up similarity calculation. Using this method, we don't need to recalculate those converged pairs' similarity. The experiments conducted on web datasets and synthetic dataset show that our new method can reduce the running time by nearly 35%.

关键词： Similarity Calculation Linkage Mining

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Combining N-gram retrieval with weights propagation on massive RDF graphs

Combining N-gram retrieval with weights propagation on massi...

引用

2012 9th International Conference on Fuzzy Systems and knowledge Discovery, FSKD 2012

作者： Hu, He Du, Xiaoyong Key Labs of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China Beijing China

ISBN: (纸本)9781467300223

N-gram approach takes the position information into account additionally and thus can offer higher accuracy in query answering than keyword based approaches and is widely used in IR and NLP. However, in large-scale RDF graphs, URIs instead of documents are the ranking and querying units;URIs are usually much shorter than documents, and different URIs are interlinked into a massive network. One shot n-gram querying is usually not good for the RDF data in many cases. In this paper, we present a hybrid framework which combines the n-gram retrieval with link analysis based weight propagation. The idea is to exploit the link structures in the RDF data graphs and propagate the one shot n-gram score weights along with these links. Large scale experiments using MapReduce on Billion Triples Challenge dataset show the hybrid framework achieves an 80.3% improvement in relevance scores over mere n-gram retrieval. © 2012 IEEE.

关键词： MapReduce

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Law text clustering based on referential relations

Law text clustering based on referential relations

引用

5th Annual ChinaGrid Conference, ChinaGrid 2010

作者： Fan, Biao Liu, Tao Hu, He Du, Xiaoyong Key Labs of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China Beijing China

ISBN: (纸本)9780769541068

This paper proposes a new method to cluster law texts based on referential relation of laws. We extract law entities (an entity represents a law) and their referential relation from law texts. Then SimRank algorithm is applied to calculate law entity's similarity through referential relation and law clustering is carried out based on the SimRank similarity. This is the first time to apply SimRank algorithm in the domain of Law and use it to carry out text clustering. Prototype and experiments show that our solution is feasible. We also publish the extracted data as Linked Law data with RDF data model, which forms the first open semantic web database in Law domain. Linked Law data enables user to access law data with rich data links and query web data by application interface of Semantic Web. © 2010 IEEE.

关键词： Cluster analysis

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Extracting domain-relevant term using wikipedia based on random walk model

Extracting domain-relevant term using wikipedia based on ran...

引用

7th ChinaGrid Annual Conference, ChinaGrid 2012

作者： Wu, Wenjuan Liu, Tao Hu, He Du, Xiaoyong Key Labs of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China Beijing China

ISBN: (纸本)9780769548166

In this paper we present a new approach for the automatic identification of domain-relevant concepts and entities of a given domain using the category and page structures of the Wikipedia in a language independent way. By applying Markov random walk algorithm on the weighted Wikipedia link graph, our approach can identify large quantities of domain-relevant concepts and entities with very little human effort. Experimental results show that our method achieves high accuracy and acceptable efficiency in domainrelevant term extraction. © 2012 IEEE.

关键词： Markov processes

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Radical features for Chinese text classification

Radical features for Chinese text classification

引用

2012 9th International Conference on Fuzzy Systems and knowledge Discovery, FSKD 2012

作者： Hu, He Du, Xiaoyong Key Labs of Data Engineering and Knowledge Engineering Ministry of Education China School of Infonnation Renmin University of China Beijing China

ISBN: (纸本)9781467300223

Chinese radicals play important roles in forming Chinese character's semantic meaning. The semantic properties of radicals make them a promising source of information to be analyzed in text mining and content extraction. However, until recently there is little research work concentrating on using the radical set in text mining related tasks. We investigate the roles of radicals in Chinese text classification tasks. In the task, texts are transformed into vectors of radicals, characters and words. Radicals are further pruned by their semantic strengths and network traits. We carry out experiments with real data from Open Directory Project. The experiments results justify Chinese radicals as important features for semantic processing in Chinese text mining tasks. © 2012 IEEE.

关键词： Semantics

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Extract knowledge from semi-structured websites for search task simplification 11

Extract knowledge from semi-structured websites for search t...

引用

20th ACM Conference on Information and knowledge Management, CIKM'11

作者： Gu, Yingqin Yan, Jun Liu, Hongyan He, Jun Ji, Lei Liu, Ning Chen, Zheng Key Labs. of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing China Microsoft Research Asia Beijing China Department of Management Science and Engineering Tsinghua University Beijing China

ISBN: (纸本)9781450307178

Simplifying the key tasks of search engine users by directly retrieving to them structured knowledge according to their queries is attracting much attention from both industry and academia. A bottleneck of this challenging problem is how to extract the structured knowledge from the noisy and complex Web scale websites automatically. In this paper, we propose an unsupervised automatic wrapper induction algorithm, named as Scalable knowledge Extractor from webSites (SKES). SKES induces the wrapper in a divide and conquer mode, i.e., it divides the general wrapper into several sub-wrappers to learn from the data independently. Moreover, through employing techniques such as tag path representation of Web pages, SKES is verified to be efficient and noise-tolerant by the experimental results. Furthermore, based on our automatically extracted knowledge, we also built a prototype to serve structured knowledge to end users for simplifying their key search tasks. Very positive feedbacks were received on the prototype. © 2011 ACM.

关键词： Websites

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