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Symposium on Learning, knowledge, and Adaptivity 2011, LWA 2011

作者： Benz, Dominik Körner, Christian Hotho, Andreas Stumme, Gerd Strohmaier, Markus Knowledge and Data Engineering Group University of Kassel Germany Knowledge Management Institute Graz University of Technology Austria Data Mining and Information Retrieval Group University of Wurzburg Germany

Recent research has demonstrated how the widespread adoption of collaborative tagging systems yields emergent semantics. In recent years, much has been learned about how to harvest the data produced by taggers for engineering light-weight ontologies. For example, existing measures of tag similarity and tag relatedness have proven crucial step stones for making latent semantic relations in tagging systems explicit. However, little progress has been made on other issues, such as understanding the different levels of tag generality (or tagabstrcatsness), which is essential for, among others, identifying hierarchical relationships between concepts. In this paper we aim to address this gap. Starting from a review of linguistic definitions of wordabstrcatness, we first use several large-scale ontologies and taxonomies as grounded measures of word generality, including Yago, Wordnet, DMOZ and Wikitaxonomy. Then, we introduce and apply several folksonomy-based methods to measure the level of generality of given tags. We evaluate these methods by comparing them with the grounded measures. Our results suggest that the generality of tags in social tagging systems can be approximated with simple measures. Our work has implications for a number of problems related to social tagging systems, including search, tag recommendation, and the acquisition of light-weight ontologies from tagging data.

关键词： Ontology

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Preventing Recommendation Attack in Trust-Based Recommender Systems

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Journal of Computer Science & Technology 2011年第5期26卷 823-828页

作者：张富国 School of Information and Technology Jiangxi University of Finance and Economics Institute of Information Resource Management Jiangxi University of Finance and Economics Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics

Despite its success,similarity-based collaborative filtering suffers from some limitations,such as scalability,sparsity and recommendation *** work has shown incorporating trust mechanism into traditional collaborative filtering recommender systems can improve these *** argue that trust-based recommender systems are facing novel recommendation attack which is different from the profile injection attacks in traditional recommender *** the best of our knowledge,there has not any prior study on recommendation attack in a trust-based recommender *** analyze the attack problem,and find that ＂victim＂ nodes play a significant role in the ***,we propose a data provenance method to trace malicious users and identify the ＂victim＂ nodes as distrust users of recommender *** study of the defend method is done with the dataset crawled from Epinions website.

关键词： data lineage ＂victim＂ node attack trust propagation

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Automatic music classification for Dangdut and campursari using Nave Bayes

Automatic music classification for Dangdut and campursari us...

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2011 International Conference on Electrical engineering and Informatics, ICEEI 2011

作者： Viny, Christanti M. Kurniawan, Freddy Tony Laboratory of Knowledge Data Engineering Faculty of Information Technology Tarumanagara University Jakarta Indonesia

ISBN: (纸本)9781457707520

Music classification can be performed by classifying music according to its genre, style, mood, and others. Various methods have been implemented to automatically classify music. Naïve Bayes learning algorithm is one of the most efficient and effective classification algorithm. Dangdut and campursari music are the music often heard by Indonesian. But the classification of dangdut and campursari music is still rarely performed. In this study, we perform automatic music classification for dangdut and campursari music. We use Nave Bayes to classify music and the data was discretized based on Minimum Description Length Principle (MDLP). We used jSymbolic to extract feature from MIDI files. Currently, we use 45 features that are included in the category of instruments and pitch. This experiment produced the accuracy of 85.14%. © 2011 IEEE.

关键词： Classification (of information)

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Reduce pruning cost to accelerate multimedia kNN search over MBRs based index structures

Reduce pruning cost to accelerate multimedia kNN search over...

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3rd International Conference on Multimedia information Networking and Security, MINES 2011

作者： Li, Hui Zhang, Xiao Wang, Shan School of Information Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering Beijing China

ISBN: (纸本)9780769545592

MBR (Minimum Bounding Rectangle) has been widely used to represent multimedia data objects for multimedia indexing techniques. In kNN search, MINDIST and MINMAXDIST was the most popular pruning metrics employed by MBR based index structures. However, with the increase of dimensionality, the computation costs of above two metrics are expensive, and the involved distance computations may become the considerable component. In this paper, to improve the performance of kNN search over multimedia data, we proposed an approach to reduce the computation cost of pruning heuristics by use the cheap upper bound of MINMAXDIST instead of its precise value, and then we derive enhanced cheap heuristics based on this upper bound for kNN pruning, which are helpful to avoid visiting unnecessary data objects and MBRs. We conducted an extensive experimental study on a large real-life multimedia dataset called MSRA-MM 2.0, obtained from the commercial Bing Image search engine. The results show that the proposed approach significantly reduced the computation cost and boosted the overall performance of MBR based multimedia kNN search. © 2011 IEEE.

关键词： Cost reduction

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Lawyer information integration and recommendation by multi-source information validation

Lawyer information integration and recommendation by multi-s...

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2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011

作者： Liu, Tao Fan, Biao Hu, He Du, Xiao-Yong Key Laboratory of Data Engineering and Knowledge Engineering MOE School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9781457703065

On the internet, all-round lawyer information is located at separated information sources, which prevent web users from effective information acquisition. In order to build a unified view of separated, heterogeneous, and often redundant lawyer information, we propose a new information integration method using multi-source information cross-validation. Based on the unified integrated data, a lawyer recommendation system is built. Several key technologies are presented and evaluated, including the multi-source information acquisition and validation. Experimental results indicate the key techniques used in the system are effective for lawyer information integration and recommendation. © 2011 IEEE.

关键词： information retrieval

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Community assessment using evidence networks

Community assessment using evidence networks

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Lecture Notes in Computer Science

作者： Mitzlaff, Folke Atzmueller, Martin Benz, Dominik Hotho, Andreas Stumme, Gerd University of Kassel Knowledge and Data Engineering Group Wilhelmshöher Allee 73 34121 Kassel Germany University of Wuerzburg Data Mining and Information Retrieval Group Am Hubland 97074 Wuerzburg Germany

ISBN: (纸本)9783642235986

Community mining is a prominent approach for identifying (user) communities in social and ubiquitous contexts. While there are a variety of methods for community mining and detection, the effective evaluation and validation of the mined communities is usually non-trivial. Often there is no evaluation data at hand in order to validate the discovered groups. This paper proposes an approach for (relative) community assessment. We introduce a set of so-called evidence networks which are capturing typical interactions in social network applications. Thus, we are able to apply a rich set of implicit information for the evaluation of communities. The presented evaluation approach is based on the idea of reconstructing existing social structures for the assessment and evaluation of a given clustering. We analyze and compare the presented approach applying user data from the real-world social bookmarking application BibSonomy. The results indicate that the evidence networks reflect the relative rating of the explicit ones very well. © 2011 Springer-Verlag.

关键词： Artificial intelligence

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Efficient Duplicate Detection on Cloud Using a New Signature Scheme

Efficient Duplicate Detection on Cloud Using a New Signature...

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12th International Conference on Web-Age information Management

作者： Rong, Chuitian Lu, Wei Du, Xiaoyong Zhang, Xiao Key Labs. of Data Engineering and Knowledge Engineering MOE China School of Information Renmin University of China China Shanghai Key Laboratory of Intelligent Information Processing China

ISBN: (纸本)9783642235344;9783642235351

Duplicate detection has been well recognized as a crucial task to improve the quality of data. Related work on this problem mainly aims to propose efficient approaches over a single machine. However, with increasing volume of the data, the performance to identify duplicates is still far from satisfactory. Hence, we try to handle the problem of duplicate detection over MapReduce, a share-nothing paradigm. We argue the performance of utilizing MapReduce to detect duplicates mainly depends on the number of candidate record pairs. In this paper, we proposed a new signature scheme with new pruning strategy over MapReduce to minimize the number of candidate record pairs. Our experimental results over both real and synthetic datasets demonstrate that our proposed signature based method is efficient and scalable.

关键词： duplicate detection MapReduce Cloud

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Improving performance by creating a native join-index for OLAP

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Frontiers of Computer Science 2011年第2期5卷 236-249页

作者： Yansong ZHANG Shan WANG Jiaheng LU National Survey Research Center at Renmin University of China Beijing 100872 China Key Laboratory of the Ministry of Education for Data Engineering and Knowledge Engineering Renmin University of China Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

The performance of online analytical processing （OLAP） is critical for meeting the increasing requirements of massive volume analytical applications. Typical techniques, such as in-memory processing, column-storage, and join indexes focus on high perfor- mance storage media, efficient storage models, and reduced query processing. While they effectively perform OLAP applications, there is a vital limitation： main- memory database based OLAP （MMOLAP） cannot provide high performance for a large size data set. In this paper, we propose a novel memory dimension table model, in which the primary keys of the dimension table can be directly mapped to dimensional tuple addresses. To achieve higher performance of dimensional tuple access, we optimize our storage model for dimension tables based on OLAP query workload features. We present directly dimensional tuple accessing （DDTA） based join （DDTA- JOIN）, a technique to optimize query processing on the memory dimension table by direct dimensional tuple access. We also contribute by proposing an optimization of the predicate tree to shorten predicate operation length by pruning useless predicate processing. Our experimental results show that the DDTA-JOIN algorithm is superior to both simulated row-store main memory query processing and the open-source column-store main memory database MonetDB, thanks to the reduced join cost and simple yet efficient query processing.

关键词： directly dimensional tuple accessing (DDTA) – DDTA JOIN – native join index – predicate tree

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Using New Fusion Operations to Improve Trust Expressiveness of Subjective Logic

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Wuhan University Journal of Natural Sciences 2011年第5期16卷 376-382页

作者： ZHOU Hongwei1,2,3,SHI Wenchang1,2,LIANG Zhaohui1,2,LIANG Bin1,2 1.Key laboratory of data engineering and knowledge engineering,Ministry of Education,Beijing 100872,China 2.School of information,Renmin University of China,Beijing 100872,China 3.Institute of Electronic Technology,information engineering University of PLA,Zhengzhou 450004,Henan,China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing China School of Information Renmin University of China Beijing China Institute of Electronic Technology Information Engineering University of PLA Zhengzhou Henan China

Subjective logic provides a means to describe the trust relationship of the realworld. However, existing fusion operations it offers Weal fused opiniotts equally, which makes it impossible to deal with the weighted opinions effectively. A. Jcsang presents a solution, which combines the discounting operator and the fusion operator to produce the consensus to the problem. In this paper, we prove that this approach is unsuitable to deal with the weighted opinions because it increases the uncertainty of the consensus. To address the problem, we propose two novel fusion operators that are capable of fusing opinions according to the weight of opinion in a fair way, and one of the strengths of them is improving the trust expressiveness of subjective logic. Furthermore, we present the justification on their definitions with the mapping between the evidence space and the opinion space. Comparisons between existing operators and the ones we proposed show the effectiveness of our new fusion operations.

关键词： trust subjective logic opinion fusion weizht

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Privacy-aware spam detection in social bookmarking systems 11

Privacy-aware spam detection in social bookmarking systems

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11th International Conference on knowledge Management and knowledge Technologies, i-KNOW 2011

作者： Bullock, Beate Navarro Lerch, Hana Roßnagel, Alexander Hotho, Andreas Stumme, Gerd University of Kassel Knowledge and Data Engineering Group Kassel Germany University of Kassel Research Group for Constitutionally Compatible Technology Design Kassel Germany University of Würzburg Data Mining and Information Retrieval Group Würzburg Germany

ISBN: (纸本)9781450307321

With the increased popularity of Web 2.0 services in the last years data privacy has become a major concern for users. The more personal data users reveal, the more difficult it becomes to control its disclosure in the web. However, for Web 2.0 service providers, the data provided by users is a valuable source for offering effective, personalised data mining services. One major application is the detection of spam in social bookmarking systems: in order to prevent a decrease of content quality, providers need to distinguish spammers and exclude them from the system. They thereby experience a conflict of interests: on the one hand, they need to identify spammers based on the information they collect about users, on the other hand, they need to respect privacy concerns and process as few personal data as possible. It would therefore be of tremendous help for system developers and users to know which personal data are needed for spam detection and which can be ignored. In this paper we address these questions by presenting a data privacy aware feature engineering approach. It consists of the design of features for spam classification which are evaluated according to both, performance and privacy conditions. Experiments using data from the social bookmarking system BibSonomy show that both conditions must not exclude each other.

关键词： data privacy

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