Xiaofeng Zhou

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Xiaofeng Zhou is an author.


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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
Social relation extraction based on Chinese Wikipedia articles Chinese Wikipedia Article
Social Relation Extraction
Social Relation Network
Lecture Notes in Computer Science English 2013 Our work in this paper pays more attention to information extraction about social relations from Chinese Wikipedia articles and construction of social relation network. After obtaining the Chinese Wikipedia articles according to the provided person name, locating the relationship description sentences in the Chinese Wikipedia articles and extracting the social relation information based on the sentence semantic parser, we can construct the social network centered with the provided person name, using the social relation information. The relation set also can be iteratively expanded based on the person names associated with the provided person name in the related Chinese Wikipedia articles. 0 0
Research on spreading and sharing of knowledge from sciencepaper online to Wikipedia Knowledge spreading
Ontology Mapping
Lecture Notes in Electrical Engineering English 2011 In order to enhance the utilization and effect of China's Scientificpaper Online(SO in short), this paper focuses on the observed research of ontology technology for knowledge spreading, we use significant Wikipedia Platform to encourage people's accessing and attention on online scientific resource. The research is on the basis of the topic information extracted from the scientific resource of SO using ontology, we establish the relationship between the topic information and the related items from Wikipedia, syncretize the scientific resource and the information flow of users' access. In other words, user can get access to scientific resource by correlative items in Wikipedia. We make ontology as the kernel, analyse and extract the topic mode of the SO, analyse the mode of Wikipedia resource, construct the automatic pattern recognition system, and make sure the topic information can be published intelligently to the correct location of Wikipedia, and achieve the effect of sharing the resource. 0 0
A survival modeling approach to biomedical search result diversification using wikipedia Biomedical IR
Survival modeling
SIGIR English 2010 0 0
Exploring Flickr's related tags for semantic annotation of web images Conditional random field model
Flickr's tag
Keyword correlation
Web image annotation
CIVR 2009 - Proceedings of the ACM International Conference on Image and Video Retrieval English 2009 Exploring social media resources, such as Flickr and Wikipedia to mitigate the difficulty of semantic gap has attracted much attention from both academia and industry. In this paper, we first propose a novel approach to derive semantic correlation matrix from Flickr's related tags resource. We then develop a novel conditional random field model for Web image annotation, which integrates the keyword correlations derived from Flickr, and the textual and visual features of Web images into an unified graph model to improve the annotation performance. The experimental results on real Web image data set demonstrate the effectiveness of the proposed keyword correlation matrix and the Web image annotation approach. Copyright 2009 ACM. 0 0
Extracting semantic relationships between wikipedia categories CEUR Workshop Proceedings English 2006 The Wikipedia is the largest online collaborative knowledge sharing system, a free encyclopedia. Built upon traditional wiki architectures, its search capabilities are limited to title and full-text search. We suggest that semantic information can be extracted from Wikipedia by analyzing the links between categories. The results can be used for building a semantic schema for Wikipedia which could improve its search capabilities and provide contributors with meaningful suggestions for editing theWikipedia pages.We analyze relevant measures for inferring the semantic relationships between page categories of Wikipedia. Experimental results show that Connectivity Ratio positively correlates with the semantic connection strength. 0 0