Fei Zhao

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Fei Zhao is an author.


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
Trendspedia: An Internet observatory for analyzing and visualizing the evolving web Proceedings - International Conference on Data Engineering English 2014 The popularity of social media services has been innovating the way of information acquisition in modern society. Meanwhile, mass information is generated in every single day. To extract useful knowledge, much effort has been invested in analyzing social media contents, e.g., (emerging) topic discovery. With these findings, however, users may still find it hard to obtain knowledge of great interest in conformity with their preference. In this paper, we present a novel system which brings proper context to continuously incoming social media contents, such that mass information can be indexed, organized and analyzed around Wikipedia entities. Four data analytics tools are employed in the system. Three of them aim to enrich each Wikipedia entity by analyzing the relevant contents while the other one builds an information network among the most relevant Wikipedia entities. With our system, users can easily pinpoint valuable information and knowledge they are interested in, as well as navigate to other closely related entities through the information network for further exploration. 0 0
Expanding approach to information retrieval using semantic similarity analysis based on wordnet and wikipedia Information retrieval
Pseudo-relevance feedback
Query expansion
Semantic similarity
International Journal of Software Engineering and Knowledge Engineering English 2012 Performance of information retrieval (IR) systems greatly relies on textual keywords and retrieval documents. Inaccurate and incomplete retrieval results are always induced by query drift and ignorance of semantic relationship among terms. Expanding retrieval approach attempts to incorporate expansion terms into original query, such as unexplored words combing from pseudo-relevance feedback (PRF) or relevance feedback documents semantic words extracting from external corpus etc. In this paper a semantic analysis-based query expansion method for information retrieval using WordNet and Wikipedia as corpus are proposed. We derive semantic-related words from human knowledge repositories such as WordNet and Wikipedia, which are combined with words filtered by semantic mining from PRF document. Our approach automatically generates new semantic-based query from original query of IR. Experimental results on TREC datasets and Google search engine show that performance of information retrieval can be significantly improved using proposed method over previous results. 0 0
Human dynamics analysis in online collaborative writing Human dynamics
Multi-scale property
Online collaborative writing
Wuli Xuebao/Acta Physica Sinica Chinese 2011 Investigating the human online behavior has become a central issue for understanding human dynamics in recent years. In this paper we analyze the temporal and content-updating statistical properties of online collaborative writing based on Wikipedia data. Online collaborative writing is one of the important and widespread human online behaviors, which is of great apphication. Empirical result shows that the distribution of inter-event time in collaborative writing is on the multi-scale. That is to say, two time intervals that range from 1 min to 30 min and 30 min to 24 h both obey power-law distribution with exponents equal to 1.62 and 1.16 respectively, while the interval larger than 24 h obeys a distribution whose cumulative form is F(τ)∝τ-b-alog(τ). More investigatons show successive updating behavior and mutual updating behavior working together to lead to the multi-scale distribution of inter-event time. Successive updating behavior leads to the power-law distribution with an exponent 1.62 of interval within 30 min while mutual updating behavior leads to the power-law distribution with an exponent 1.16 of interval ranging from 30 min to 24 h. Furthermore, we find that reverse updating repeats frequently in collaborative writing. The proportions of reversing updating and the updating size are strongly relatively reflect that the updating size is a main reason leading to the relevant content to be preserved. The bigger the updating size, the harder it would be preserved. More statistical analyses imply that "watching dog" and "edit war" exist in Wikipedia editing. Those results are very helpful to deepen the understanding of the human collective behavior, especially of the collaborative developing behavior. 0 0
Research progress on wikipedia Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China 2010 The rapid development of web technology has promoted the emergence and organization of the collaborative Wiki systems. This paper introduces the Wikipedia's history, macro-level statistical properties, evolution regularities, and so on. Especially the application of the motivation and methods of complex network study in analyzing the Wikipedia is emphasized. Wikipedia's significance and impacts on society, economy, culture and education are also discussed. Finally, some open questions are outlined for future research; especially the connection between Wikipedia and the new development in complexity sciences, such as the studies of complex network and human dynamics. 0 0