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Annals of data Science 2017年第4期4卷 433-440页

作者： Shi, Yong Shan, Zhiguang Li, Jianping Fang, Yufei School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Informatization Research Department State Information Center Beijing100045 China Institute of Policy and Management Chinese Academy of Sciences Beijing100190 China School of Computer and Communication Engineering University of Science and Technology Beijing100083 China Informatization Research Department State Information Center Beijing100045 China

On September 5, 2015, the State Council of Chinese Government, China’s cabinet formally announced its Action Framework for Promoting Big data (***, 2015). This is the milestone for China to catch up the global wave of big data. Since 2012 big data became a hot issue for scientific communities as well as the governments of many countries (Lazer et al. in Science 343:1203–1205, 2014;Einav et al. in Science 345:715, 2014;Cate in Science 346:818, 2014;Khoury and Ioannidis in Science 346:1054–1055, 2014). At the 2013 G8 Summit, the leaders of Canada, France, Germany, Italy, Japan, Russia, U.S.A. and United Kingdom agreed on an "open government plan" (***/government/publications/open-data-charter/g8-open-data-charter-and-technical-annex, 2013). China’s framework, however, mainly emphasizes the integration of all trans-departmental data and establishes a number of government-driven national big data platforms so as to provide big data services to research, public and enterprises. The framework not only demonstrates a strong commitment of the Chinese government on big data, but also covers a wide range of governmental branches, enterprises and institutions far more than that of other countries. In addition, the framework shows an interpretation of big data that differs from other countries. If its objective is achieved, China would become a strong "big data country". © 2017, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Big data

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Academic publication management with PUMA - Collect, organize and share publications 1

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14th European Conference on research and Advanced Technology for Digital Libraries, ECDL 2010

作者： Benz, Dominik Hotho, Andreas Jäschke, Robert Stumme, Gerd Halle, Axel Gerlach Sanches Lima, Angela Steenweg, Helge Stefani, Sven Knowledge and Data Engineering Group University of Kassel L3S Research Center Kassel D-34121 Germany University Library of Kassel Kassel D-34127 Germany Data Mining and Information Retrieval Group University of Würzburg Am Hubland Würzburg D-97074 Germany

ISBN: (数字)9783642154645

ISBN: (纸本)3642154638

The PUMA project fosters the Open Access movement und aims at a better support of the researcher's publication work. PUMA stands for an integrated solution, where the upload of a publication results automatically in an update of both the personal and institutional homepage, the creation of an entry in a social bookmarking systems like BibSonomy, an entry in the academic reporting system of the university, and its publication in the institutional repository. In this poster, we present the main features of our solution. © 2010 Springer-Verlag Berlin Heidelberg.

关键词： Information services

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Information retrieval in folksonomies: Search and ranking

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3rd European Semantic Web Conference, ESWC 2006

作者： Hotho, Andreas Jäschke, Robert Schmilz, Christoph Stumme, Gerd Knowledge and Data Engineering Group Department of Mathematics and Computer Science University of Kassel Wilhelmshöher Allee 73 D-34121 Kassel Germany Research Center L3S Expo Plaza 1 D-30539 Hannover Germany

ISBN: (纸本)3540345442

Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. The reason for their immediate success is the fact that no specific skills are needed for participating. At the moment, however, the information retrieval support is limited. We present a formal model and a new search algorithm for folksonomies, called FolkRank, that exploits the structure of the folksonomy. The proposed algorithm is also applied to find communities within the folksonomy and is used to structure search results. All findings are demonstrated on a large scale dataset. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Information retrieval systems

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08391 - Working Group Summary Analyzing Tag Semantics Across Collaborative Tagging Systems - Dagstuhl Seminar - Dagstuhl S

08391 - Working Group Summary Analyzing Tag Semantics Across...

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Social Web Communities 2008

作者： Benz, Dominik Grobelnik, Marko Hotho, Andreas Jäschke, Robert Mladenic, Dunja Servedio, Vito D. P. Sizov, Sergej Szomszor, Martin Knowledge & Data Engineering Group University of Kassel Germany Department of Knowledge Technologies J. Stefan Institute Ljubljana Slovenia Research Center L3S Hannover Germany La Sapienza University of Rome Italy Information Systems and Semantic Web University of Koblenz-Landau Germany University of Southampton United Kingdom

The objective of our group was to exploit state-of-the-art Information Retrieval methods for finding associations and dependencies between tags, capturing and representing differences in tagging behavior and vocabulary of various folksonomies, with the overall aim to better understand the semantics of tags and the tagging process. Therefore we analyze the semantic content of tags in the Flickr and Delicious folksonomies. We find that: tag context similarity leads to meaningful results in Flickr, despite its narrow folksonomy character;the comparison of tags across Flickr and Delicious shows little semantic overlap, being tags in Flickr associated more to visual aspects rather than technological as it seems to be in Delicious;there are regions in the tag-tag space, provided with the cosine similarity metric, that are characterized by high density;the order of tags inside a post has a semantic relevance. © 2008 Dagstuhl Seminar Proceedings. All rights reserved.

关键词： Semantics

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Records systems and information systems: Connecting in organizations 8th

Records systems and information systems: Connecting in organ...

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8th International Joint Conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2016

作者： Xie, Sherry L. Fan, Guanyan Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education of China Beijing100872 China Center for Electronic Records Management Research Beijing100872 China School of Information Resource Management Renmin University of China 59 Zhongguancun Ave Haidian District Beijing100872 China

ISBN: (纸本)9783319997001

The field of Information Systems (ISs) has long been recognized, so has Enterprise Information Systems (EISs), a field close to it. Long existing also in organizations or enterprises is the field of records management, now predominantly digital records management (DRM), which shares the many goals of ISs and EISs in supporting the operation and success of organizations. While the DRM field recognized rather early in its battle to digital records challenges that the need to establish formal collaborations with the ISs and EISs professions, it is still rare to spot today discussions regarding such collaborations in the general information and communication technology (ICT) literature. To help bridge the gap, this chapter introduces to the ISs and EISs professions one of the major developments of the international DRM field, that is, the records systems elaborated by the InterPARES (International research on Permanent and Authentic Records in Electronic Systems) project, for the purpose of invoking further discussions. © Springer Nature Switzerland AG 2019.

关键词： Information systems

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Emergent semantics in BibSonomy

Emergent semantics in BibSonomy

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36th Jahrestagung der Gesellschaft fur Informatik e.V. (GI): Informatik fur Menschen, INFORMATIK 2006

作者： Hotho, Andreas Jäschke, Robert Schmitz, Christoph Stumme, Gerd Knowledge and Data Engineering Group Department of Mathematics and Computer Science University of Kassel Wilhelmshöher Allee 73 D-34121 Kassel Germany Research Center L3S Expo Plaza 1 D-30539 Hannover Germany

ISBN: (纸本)9783885791881

关键词： Semantics

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China’s NTB Market: A Liquidity Dilemma

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Journal of Systems Science & Complexity 2018年第2期31卷 493-507页

作者： LI Yumeng MENG Xiangying ZHANG Yueyan WEI Xianhua School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Key Laborutory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China Sino-Danish Center for Education and Research University of Chinese Academy of Sciences Beijing 100190 China The State Key Laboratory of Automotive Safety and Energy Tsinghua University Beijing 100084 China Department of Automotive Engineering Tsinghua University Beijing 100084 China

This paper introduces China＇s New Third Board Market（the NTB market） and tests the market-making system in the role of improving market liquidity. The NTB Market is the main part of China＇s OTC market, established in 2006. In comparing with China＇s Main Board Market and the Second Board Market, it attracts a lot of start-up companies that need financing with less strict listing requirements. Meanwhile, it is full of opportunities and challenges that appeal to numerous securities traders and investors with the rapid development momentum. Since its establishment, the NTB Market has been stuck in the lack of liquidity. The authority of the NTB Market introduced market maker system, so as to solve the problem of liquidity. Using the difference-in-difference method（DID method）, the market maker system has been proved to make little contribution to improving the stock liquidity. Although the problem of liquidity is difficult to solve in short term, the NTB Market is full of opportunities for all kinds of participants definitely.

关键词： China's stock market DID method liquidity market makers.

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Summary of the 15th Discovery Challenge - Recommending given names 15

Summary of the 15th Discovery Challenge - Recommending given...

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15th ECML PKDD Discovery Challenge - Recommending Given Names, 20DC13 2013

作者： Mitzlaff, Folke Doerfel, Stephan Hotho, Andreas Jäschke, Robert Mueller, Juergen University of Kassel Knowledge Discovery and Data Engineering Group Wilhelmshöher Allee 73 Kassel34121 Germany University of Würzburg Data Mining and Information Retrieval Group Am Hubland Würzburg97074 Germany L3S Research Center Appelstraße 4 Hannover30167 Germany

The 15th ECML PKDD Discovery Challenge centered around the recommendation of given names. Participants of the challenge implemented algorithms that were tested both offline - on data collected by the name search engine Nameling - and online within Nameling. Here, we describe both tasks in detail and discuss the publicly available datasets. We motivate and explain the chosen evaluation of the challenge, and we summarize the different approaches applied to the name recommendation tasks. Finally, we present the rankings and winners of the offline and the online phase.

关键词： Search engines

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Stable Learning via Triplex Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 5267-5276页

作者： Yang, Shuai Jiang, Tingting Dang, Qianlong Gu, Lichuan Wu, Xindong Anhui Agricultural University School of Information and Artificial Intelligence Hefei230036 China Anhui Provincial Engineering Research Center for Agricultural Information Perception and Intelligent Computing Hefei230036 China Northwest A & F University College of Science Yangling712100 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei230601 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China

Stable learning aims to learn a model that generalizes well to arbitrary unseen target domain by leveraging a single source domain. Recent advances in stable learning have focused on balancing the distribution of confounders for each feature to eliminate spurious correlations. However, previous studies treat all features equally without considering the difficulties of confounder balancing associated with different features, and regard irrelevant features as confounders, deteriorating generalization performance. To tackle these issues, this article proposes a novel triplex learning (TriL) based stable learning algorithm, which performs sample reweighting, causal feature selection, and representation learning to remove spurious correlations. Specifically, first, TriL adaptively assigns weights to the confounder balancing term of each feature in accordance with the difficulties of confounder balancing, and aligns the confounder distribution of each feature by learning a group of sample weights. Second, TriL integrates the sample weights into a weighted cross-entropy model to compute causal effects of features for excluding irrelevant features from the confounder set. Finally, TriL relearns a set of sample weights and uses them to guide a new supervised dual-autoencoder containing two classifiers to learn feature representations. TriL forces the results of two classifiers to remain consistent for removing spurious correlations by using a cross-classifier consistency regularization. Extensive experiments on synthetic and two real-world datasets show the superiority of TriL compared with seven methods. © 2024 IEEE.

关键词： Feature Selection

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Folkrank: A ranking algorithm for folksonomies

Folkrank: A ranking algorithm for folksonomies

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Workshop-Woche on Lernen, Wissen und Adaptivitat, LWA 2006

作者： Hotho, Andreas Jäschke, Robert Schmitz, Christoph Stumme, Gerd Knowledge and Data Engineering Group Department of Mathematics and Computer Science University of Kassel Wilhelmshöher Allee 73 D34121 Kassel Germany Research Center L3S Expo Plaza 1 D30539 Hannover Germany

In social bookmark tools users are setting up lightweight conceptual structures called folksonomies. Currently, the information retrieval support is limited. We present a formal model and a new search algorithm for folksonomies, called FolkRank, that exploits the structure of the folksonomy. The proposed algorithm is also applied to find communities within the folksonomy and is used to structure search results. All findings are demonstrated on a large scale dataset. A long version of this paper has been published at the European Semantic Web Conference 2006 [3].

关键词： Semantic Web

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