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Shen, R.; Wang, J.; Fox, E.A.: ¬A Lightweight Protocol between Digital Libraries and Visualization Systems (2002) 0.09

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Date: 22. 2.2003 17:25:39
22. 2.2003 18:15:14

Wang, J.; Reid, E.O.F.: Developing WWW information systems on the Internet (1996) 0.02

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Abstract: Gives an overview of Web information system development. Discusses some basic concepts and technologies such as HTML, HTML FORM, CGI and Java, which are associated with developing WWW information systems. Further discusses the design and implementation of Virtual Travel Mart, a Web based end user oriented travel information system. Finally, addresses some issues in developing WWW information systems

Wang, J.; Clements, M.; Yang, J.; Vries, A.P. de; Reinders, M.J.T.: Personalization of tagging systems (2010) 0.02
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Abstract

Social media systems have encouraged end user participation in the Internet, for the purpose of storing and distributing Internet content, sharing opinions and maintaining relationships. Collaborative tagging allows users to annotate the resulting user-generated content, and enables effective retrieval of otherwise uncategorised data. However, compared to professional web content production, collaborative tagging systems face the challenge that end-users assign tags in an uncontrolled manner, resulting in unsystematic and inconsistent metadata. This paper introduces a framework for the personalization of social media systems. We pinpoint three tasks that would benefit from personalization: collaborative tagging, collaborative browsing and collaborative search. We propose a ranking model for each task that integrates the individual user's tagging history in the recommendation of tags and content, to align its suggestions to the individual user preferences. We demonstrate on two real data sets that for all three tasks, the personalized ranking should take into account both the user's own preference and the opinion of others.

Hicks, D.; Wang, J.: Coverage and overlap of the new social sciences and humanities journal lists (2011) 0.01

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Date: 22. 1.2011 13:21:28

He, R.; Wang, J.; Tian, J.; Chu, C.-T.; Mauney, B.; Perisic, I.: Session analysis of people search within a professional social network (2013) 0.01

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Date: 19. 4.2013 20:31:22

Jiang, Z.; Gu, Q.; Yin, Y.; Wang, J.; Chen, D.: GRAW+ : a two-view graph propagation method with word coupling for readability assessment (2019) 0.01

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Date: 15. 4.2019 13:46:22

Wang, J.; Halffman, W.; Zhang, Y.H.: Sorting out journals : the proliferation of journal lists in China (2023) 0.01

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Date: 22. 9.2023 16:39:23

Wang, J.: ¬An extensive study on automated Dewey Decimal Classification (2009) 0.01
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Abstract

In this paper, we present a theoretical analysis and extensive experiments on the automated assignment of Dewey Decimal Classification (DDC) classes to bibliographic data with a supervised machine-learning approach. Library classification systems, such as the DDC, impose great obstacles on state-of-art text categorization (TC) technologies, including deep hierarchy, data sparseness, and skewed distribution. We first analyze statistically the document and category distributions over the DDC, and discuss the obstacles imposed by bibliographic corpora and library classification schemes on TC technology. To overcome these obstacles, we propose an innovative algorithm to reshape the DDC structure into a balanced virtual tree by balancing the category distribution and flattening the hierarchy. To improve the classification effectiveness to a level acceptable to real-world applications, we propose an interactive classification model that is able to predict a class of any depth within a limited number of user interactions. The experiments are conducted on a large bibliographic collection created by the Library of Congress within the science and technology domains over 10 years. With no more than three interactions, a classification accuracy of nearly 90% is achieved, thus providing a practical solution to the automatic bibliographic classification problem.
Qiu, J.; Zuo, M.; Wang, J.; Cai, C.: Knowledge order in an online knowledge community : group heterogeneity and two paths mediated by group interaction (2021) 0.01
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Abstract

Knowledge order in an online knowledge community (OKC) refers to a consensual version of collective knowledge in the creation of shared knowledge representation. Much previous research has been conducted in the context of the ordered structure of objective knowledge systems, but this does little to explain the microlevel order of knowledge after users contribute knowledge and achieve consensus through online interactions in OKC. Based on interactive team cognition theory and the stigmergy coordination mechanism, our research aims to investigate how knowledge and experience heterogeneity affect knowledge order effectiveness and efficiency through collaborative and communicative interaction. To test our hypotheses, we randomly collected the records of 250 articles from the English version of Wikipedia. Partial least squares structural equation modeling indicated that OKC favoring online collective knowledge order by limiting communicative interaction, as collaborative interaction is very effective in achieving knowledge order and in achieving it in a fast way. From our findings, scholars and practitioners are advised to pay attention to online knowledge order in the management and design of OKC.

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