Haisu Zhang

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Haisu Zhang is an author.

Publications

Only those publications related to wikis are shown here.
Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Extracting traffic information from web texts with a D-S evidence theory based approach D-S evidence theory
Text clustering
Traffic state
Web texts
Wikipedia
International Conference on Geoinformatics English 2013 Web texts, such as web pages, BBS, or microblogs, usually contain a great amount of real-time traffic information, which can be expected to become an important data source for city traffic collection. However, due to the characteristics of ambiguity and uncertainty in the description of traffic condition with natural language, and the difference of description quality for web texts among various publishers and text types, there may exist much inconsistency, or even contradiction for the traffic condition on similar spatial-temporal contexts. An efficient information fusion process is crucial to take advantage of the mass web sources for real-time traffic collection. In this paper, we propose a traffic state extraction approach from massive web texts based on D-S evidence theory to solve the above problem. Firstly, an evaluation index system for the traffic state information collected from the web texts is built with the help of semantic similarity based on Wikipedia, to eliminate ambiguity. Then, D-S evidence theory is adopted to judge and fuse the extracted traffic state information, with evidence combination and decision, which can solve the problem of uncertainty and difference. An experiment shows that the presented approach can effectively judge the traffic state information contained in massive web texts, and can fully utilize the data from different websites. Meanwhile, the proposed approach is arguably more accurate than the traditional text clustering algorithm. 0 0
Position-wise contextual advertising: Placing relevant ads at appropriate positions of a web page Contextual advertising
Similarity
Wikipedia knowledge
Neurocomputing English 2013 Web advertising, a form of online advertising, which uses the Internet as a medium to post product or service information and attract customers, has become one of the most important marketing channels. As one prevalent type of web advertising, contextual advertising refers to the placement of the most relevant ads at appropriate positions of a web page, so as to provide a better user experience and increase the user's ad-click rate. However, most existing contextual advertising techniques only take into account how to select as relevant ads for a given page as possible, without considering the positional effect of the ad placement on the page, resulting in an unsatisfactory performance in ad local context relevance. In this paper, we address the novel problem of position-wise contextual advertising, i.e., how to select and place relevant ads properly for a target web page. In our proposed approach, the relevant ads are selected based on not only global context relevance but also local context relevance, so that the embedded ads yield contextual relevance to both the whole target page and the insertion positions where the ads are placed. In addition, to improve the accuracy of global and local context relevance measure, the rich wikipedia knowledge is used to enhance the semantic feature representation of pages and ad candidates. Last, we evaluate our approach using a set of ads and pages downloaded from the Internet, and demonstrate the effectiveness of our approach. © 2013 Elsevier B.V. 0 0
Exploration and visualization of administrator network in wikipedia Human factors
Social network analysis
Visualisation
Wikipedia
Lecture Notes in Computer Science English 2012 Wikipedia has become one of the most widely used knowledge systems on the Web. It contains the resources and information with different qualities contributed by different set of authors. A special group of authors named administrators plays an important role for content quality in Wikipedia. Understanding the behaviors of administrators in Wikipedia can facilitate the management of Wikipedia system, and empower some applications such as article recommendation and expertise administrator finding for given articles. This paper addresses the work of the exploration and visualization of the administrator network in Wikipedia. Administrator network is firstly constructed by using co-editing relationship and six characteristics for administrators are proposed to describe the behaviors of administrators in Wikipedia from different perspectives. Quantified calculation of these characteristics is then put forwarded by using social network analysis techniques. Topic model is used to relate content of Wikipedia to the interest diversity of administrators. Based on the media wiki history records from the January 2010 to January 2011, we develop an administrator exploration prototype system which can rank the selected characteristics for administrators and can be used as a decision support system. Furthermore, some meaningful observations are found to show that the administrator network is a healthy small world community and a strong centralization of the network around some hubs/stars is obtained to mean a considerable nucleus of very active administrators that seems to be omnipresent. These top ranked administrators ranking is found to be consistent with the number of barn stars awarded to them. 0 0
Inferring individual influence in social network Factor Graph
Inferring model
Social Network
Proceedings - 9th Web Information Systems and Applications Conference, WISA 2012 English 2012 We study the integration of individuals attributes to infer their influence ability in social network in this paper. The influence between individuals is usually asymmetric and can propagate via edges gradually. We suggest an Influence Factor Graph(IFG) which can integrate different node and edge features into a uniform inferring model. And for each node the model can compute personalized influence ability value. Experiment results in Zarchary and Wikipedia co-editing social networks show that, the model can depict influence reasonably and reveal some interesting social influence rules. 0 0
Survey on statics of Wikipedia Collective intelligence
Statistics
Wiki
Wikipedia
Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University Chinese 2012 This paper mainly focuses on the Wikipedia, a collaborative editing pattern in Web 2. 0. The articles, editors and the editing relationships between the two ones are three important components in Wikipedia statistical analysis. We collected different kinds of statistical tools, methods and results, and further analyzed the problems in the current statistics researches and discussed the possible resolutions. 0 0
A Research for the Centrality of Article Edit Collective in Wikipedia Wikipedia
Article edit interaction network
Centrality
Networked data mining
Collective intelligence
ICM English 2011 0 0
Quality of articles in Wikipedia Collective intelligence
Quality of article evaluation
Wiki
Wikipedia
Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University Chinese 2011 The recent research of wikipediais is firs briefly analyzed, especially on the statistics of quality of articles in Wikipedia. Then the automatic evaluating methods of article quality are discussed. The methods mainly include two kinds: the correlation-based analysis and cooperation modeling. Furthermore, we present the open problems of automatic quality evaluation and the possiblepromotions of collective intelligence. 0 0
There exist correlations between editing behaviors and hyperlinks structure in Wikipedia Human behavior dynamics
Social network analysis
Wikipedia
Proceedings - 2011 IEEE International Conference on Granular Computing, GrC 2011 English 2011 The co-editing in Wikipedia is a typical and complex collective behavior with lots of voluntary editors' participation, while the relationship between the edit behaviors and the hyperlink structure of articles remains unknown until now. In this paper, we try to explore the correlation between them via a novel two-layer network. In this two-layer network, we model the articles in Wikipedia as nodes, and model the edits and hyperlinks as the edges of two layers respectively. Here, the correlation is suggested to be measured by a structure similarity metric. By analyzing the structure similarity of two layers via a method named partially ordered ranking, we find that there exist significant and stable correlations: in our sample dataset composed of four Wikipedia categories, the structure similarity is around 0.6, which is two times than that of a theoretical random network. Furthermore, if turn back to the initial stage of categories, i.e., take the evolution into consideration, the correlation is evolving too. Usually the evolution undergoes a sharp decline stage from the initial high value, and at last it tends to the stable value around 0.6. 0 0
EachWiki: Suggest to be an easy-to-edit wiki interface for everyone CEUR Workshop Proceedings English 2007 In this paper, we present EachWiki, an extension of Semantic MediaWiki characterized by an intelligent suggestion mechanism. It aims to facilitate the wiki authoring by recommending the following elements: links, categories, and properties. We exploit the semantics of Wikipedia data and leverage the collective wisdom of web users to provide high quality annotation suggestions. The proposed mechanism not only improves the usability of Semantic MediaWiki but also speeds up its converging use of terminology. The suggestions are applied to relieve the burden of wiki authoring and attract more inexperienced contributors, thus making Semantic MediaWiki even better Semantic Web proto types and data source. 0 0