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Joint International Conference on Advances in data and Web Management, APWeb/WAIM 2009

作者： Li, Zhixu Li, Pei Wei, Wei Liu, Hongyan He, Jun Liu, Tao Du, Xiaoyong Key Labs of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China Beijing China Department of Management Science and Engineering Tsinghua University Beijing China

ISBN: (纸本)9783642006715

Nearly all text classification methods classify texts into predefined categories according to the terms appeared in texts. State-of-the-art of text classification prefer to simplely take a word as a term since it performs good on some famous datasets;some experts even pointed out that phrases don't improve or improve only marginally the classifiction accuracy. However, we found out that this is not always true when we try to categorize texts about similar topics in the same domain. With words only we can not categorize those texts effectively since they nearly share the same word set. Then we suppose the results might be improved if we also use phrases as terms. To testify our supposition, we propose our own phrase extraction way as well as select proper feature selection method and classifier by conducting experimental study on a data set which comes from paper abstracts in the field of databases. Accordingly, we also develop a system called AutoPCS which can be used to help experts in choosing relevant topics for newly coming papers from a predefined topic list only by their abstracts. © Springer-Verlag Berlin Heidelberg 2009.

关键词： Classification (of information)

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Discovering Chinese compound term using termhood and unithood measures

Proceedings - 2011 6th Annual ChinaGrid Conference, ChinaGri...

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Proceedings - 2011 6th Annual ChinaGrid Conference, ChinaGrid 2011 2011年 60-67页

作者： Kang, Jingjing 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)9780769544724

Domain terms play a crucial role in many research areas, which has led to a rise in demand for automatic domain terms extraction. In this paper, we present a two-level evaluation approach based on term hood and unit hood to extract Chinese domain compound terms automatically, which takes the character-level and word-level information into account. To achieve this, we incorporate semantic features by using the word segmentation to recognize single word terms, then leverage the improved C-value and heuristic methods such as word formation pattern and word formation power to evaluate candidates at both levels. By validating our approach with several existing dictionaries, a significant improvement of compound terms detection is achieved. Experiments in legal corpus show our method is superior over other compared methods. © 2011 IEEE.

关键词： Heuristic methods

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Deprank: A probabilistic measure of dependence via heterogeneous links

Deprank: A probabilistic measure of dependence via heterogen...

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Joint International Conference on Advances in data and Web Management, APWeb/WAIM 2009

作者： Li, Pei Hu, Bo Liu, Hongyan He, Jun Du, Xiaoyong Key Labs of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China Beijing China Department of Management Science and Engineering Tsinghua University Beijing China

ISBN: (纸本)9783642006715

Dependence is a common relationship between objects. Many works have paid their attentions on dependence, but many of them mainly focus on constructing or exploiting dependence graphs on some specific domain. In this paper, we give a generic definition of dependence and make equivalence to the situation of graphs, and propose an algorithm called DepRank, which quantifies the dependence degree of a node pair based on probabilities of all dependence paths. Empirical study shows that DepRank method has reasonable results and maintains a well balance between accuracy and efficiency. © Springer-Verlag Berlin Heidelberg 2009.

关键词： Artificial intelligence

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Efficient Algorithm for Computing Link-based Similarity in Real World Networks

Efficient Algorithm for Computing Link-based Similarity in R...

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9th IEEE International Conference on data Mining

作者： Cai, Yuanzhe Cong, Gao Jia, Xu Liu, Hongyan He, Jun Lu, Jiaheng Du, Xiaoyong Key Labs of Data Engineering and Knowledge Engineering Ministry of Education China Department of Computer Science Renmin University of China China Department of Computer Science Aalborg University Denmark Department of Management Science and Engineering Tsinghua University China

ISBN: (纸本)9781424452422

Similarity calculation has many applications, such as information retrieval, and collaborative filtering, among many others. It has been shown that link-based similarity measure, such as SimRank, is very effective in characterizing the object similarities in networks, such as the Web, by exploiting the object-to-object relationship. Unfortunately, it is prohibitively expensive to compute the link-based similarity in a relatively large graph. In this paper, based on the observation that link-based similarity scores of real world graphs follow the power-law distribution, we propose a new approximate algorithm, namely Power-SimRank, with guaranteed error bound to efficiently compute link-based similarity measure. We also prove the convergence of the proposed algorithm. Extensive experiments conducted on real world datasets and synthetic datasets show that the proposed algorithm outperforms SimRank by four-five times in terms of efficiency while the error generated by the approximation is small.

关键词： Similarity Calculation SimRank Graph Mining

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Bayesian network structure learning from attribute uncertain data

Bayesian network structure learning from attribute uncertain...

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13th International Conference on Web-Age Information Management, WAIM 2012

作者： Song, Wenting Yu, Jeffrey Xu Cheng, Hong Liu, Hongyan He, Jun Du, Xiaoyong Key Labs. of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China China Chinese University of Hong Kong Hong Kong Hong Kong Department of Manage. Sci. and Eng. Tsinghua University China

ISBN: (纸本)9783642322808

In recent years there has been a growing interest in Bayesian Network learning from uncertain data. While many researchers focus on Bayesian Network learning from data with tuple uncertainty, Bayesian Network structure learning from data with attribute uncertainty gets little attention. In this paper we make a clear definition of attribute uncertain data and Bayesian Network Learning problem from such data. We propose a structure learning method named DTAU based on information theory. The algorithm assumes that the structure of a Bayesian network is a tree. It avoids enumerating all possible worlds. The dependency tree is computed with polynomial time complexity. We conduct experiments to demonstrate the effectiveness and efficiency of our method. The experiments show the clustering results on uncertain dataset by our dependency tree are acceptable. © 2012 Springer-Verlag.

关键词： Bayesian networks

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CRO: A system for online review structurization

CRO: A system for online review structurization

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14th ACM SIGKDD International Conference on knowledge Discovery and data Mining, KDD 2008

作者： Liu, Hongyan Yang, Hui Li, Wenbo Wei, Wei He, Jun Du, Xiaoyong School of Economics and Management Tsinghua University Beijing 100084 China Information School Renmin University of China Beijing 100872 China School of Computer Science and Engineering Beihang University Beijing 100083 China Key Labs. of Data Engineering and Knowledge Engineering MOE Renmin University of China Beijing 100872 China

ISBN: (纸本)9781605581934

In this paper, we present a system called CRO (Chinese Review Observer) for online product review structurization. By Structurization, we mean identifying, extracting and summarizing information from unstructured review text to a structured table. The core tasks include review collection, product feature and user opinion extraction, and polarity analysis of opinions. Existing research in this area is mainly English text oriented. To deal with Chinese effectively, we propose several novel approaches for fulfilling the core tasks. Then we integrated these approaches and implement the whole procedure of review structurization in the system CRO. Running results for reviews of real products show its performance is satisfactory.

关键词： Sentiment analysis

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IRank: A term-based innovation ranking system for conferences and scholars

IRank: A term-based innovation ranking system for conference...

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Joint International Conference on Advances in data and Web Management, APWeb/WAIM 2009

作者： Li, Zhixu Du, Xiaoyong Liu, Hongyan He, Jun Zhou, Xiaofang Key Labs of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China Beijing China Department of Management Science and Engineering Tsinghua University Beijing China University of Queensland QLD Australia

ISBN: (纸本)9783642006715

Since the proposition of Journal Impact Factor [1] in 1963, the classical citation-based ranking scheme has been a standard criterion to rank journals and conferences. However, the reference of a paper cannot list all relevant publications and the citation relationships are not always available especially when related to copyright problem. Besides, we cannot evaluate a newly published paper before it is cited by others. Therefore, we propose an alternative method, term-based evaluation scheme which can evaluate publications by terms they use. Then we can rank conferences, journals and scholars accordingly. We think this term-based ranking scheme can be used to evaluate innovation quality for conferences and scholars. To evaluate our scheme and to facilitate its application, we develop an innovation ranking system called IRank to rank conferences and authors in the field of database Systems. The performance of IRank demonstrates the effectiveness of our scheme. © Springer-Verlag Berlin Heidelberg 2009.

关键词： Quality control

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Efficient common items extraction from multiple sorted lists

Efficient common items extraction from multiple sorted lists

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12th International Asia Pacific Web Conference, APWeb 2010

作者： Lu, Wei Rong, Chuitian Chen, Jinchuan Du, Xiaoyong Cheong Fung, Gabriel Pui Zhou, Xiaofang School of Information Renmin University of China China Key Labs of Data Engineering and Knowledge Engineering Ministry of Education China School of Computing Informatics Arizona State University United States School of ITEE University of Queensland Australia

ISBN: (纸本)9780769540122

Given a set of lists, where items of each list are sorted by the ascending order of their values, the objective of this paper is to figure out the common items that appear in all of the lists efficiently. This problem is sometimes known as common items extraction from sorted lists. To solve this problem, one common approach is to scan all items of all lists sequentially in parallel until one of the lists is exhausted. However, we observe that if the overlap of items across all lists is not high, such sequential access approach can be significantly improved. In this paper, we propose two algorithms, MergeSkip and MergeESkip, to solve this problem by taking the idea of skipping as many items of lists as possible. As a result, a large number of comparisons among items can be saved, and hence the efficiency can be improved. We conduct extensive analysis of our proposed algorithms on one real dataset and two synthetic datasets with different data distributions. We report all our findings in this paper. © 2010 IEEE.

关键词： Problem solving

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Local methods for estimating SimRank score

Local methods for estimating SimRank score

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12th International Asia Pacific Web Conference, APWeb 2010

作者： Jia, Xu Liu, Hongyan Zou, Li He, Jun Du, Xiaoyong Cai, Yuanzhe 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 Department of Computer Science University of Texas Arlington TX 76019 United States

ISBN: (纸本)9780769540122

SimRank is a well known algorithm which conducts link analysis to measure similarity between each pair of nodes (nodepair). But it suffers from high computational cost, limiting its usage in large-scale datasets. Moreover, Links between nodes are changing over time. It may be desirable to quickly approximate the similarity score between certain nodepair without performing a large-scale computation on the entire graph. In our approach we propose a method to efficiently estimate the similarity score using only a small subgraph of the entire graph. We call this novel algorithm "Local-SimRank". The experimental results conducted on real datasets and synthetic dataset show that our algorithm efficiently produces good approximations to the global SimRank scores. Meanwhile, we prove that the Local-SimRank score LS(a, b) is always less than original SimRank score S(a, b) mathematically. © 2010 IEEE.

关键词： Approximation algorithms

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MPDNet: A 3D Missing Part Detection Network Based on Point Cloud Segmentation

MPDNet: A 3D Missing Part Detection Network Based on Point C...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Zhaoxin Fan Hongyan Liu Jun He Min Zhang Xiaoyong Du Key Labs of Data Engineering and Knowledge Engineering Ministry of Education Renmin University of China China Tsinghua University China

Utilizing computer vision technologies for machinery missing part detection has been a hot research topic recently. Most of existing methods take images as input and utilize 2D object detection pipelines for detecting fault regions. However, 2D models can't handle the situation when occlusion exists. To tackle the issue, in this paper, we propose a novel model named MPDNet, which exploits 3D point cloud pairs as input for missing part detection. In MPDNet, the missing part detection problem is transformed into a binary segmentation problem. The key idea behind our approach is that difference between two point clouds can be fully perceived in the latent feature space if they are encoded by the same encoder. Specially, we firstly propose a shared encode and abnormal lift module to find and enlarge difference between the target point cloud to be diagnosed and its corresponding predefined source point cloud without any fault. Then an attention based decoder is proposed to segment the source point cloud into two clusters: points that are missing in the target point cloud and points that are preserved in the target point cloud. Meanwhile, to make the model robust, a point cloud construction module is proposed as an auxiliary task to help the shared encoder to learn more discriminative features. Experiments on both synthetic and real world datasets have demonstrated the effectiveness of our model.

关键词： Fault diagnosis Solid modeling Three-dimensional displays Pipelines Object detection Signal processing Decoding

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