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Xiangfeng Luo
Researcher at Shanghai University
Publications - 207
Citations - 2523
Xiangfeng Luo is an academic researcher from Shanghai University. The author has contributed to research in topics: Semantics & Semantic Web Stack. The author has an hindex of 22, co-authored 188 publications receiving 2233 citations. Previous affiliations of Xiangfeng Luo include Chinese Academy of Sciences & University of Science and Technology of China.
Papers
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Journal ArticleDOI
Building Association Link Network for Semantic Link on Web Resources
TL;DR: A discovery algorithm of associated resources is first proposed to build original ALN for organizing loose Web resources and an application using C-ALN to organize Web services is presented, which shows that C- ALN is an effective and efficient tool for building semantic link on the resources of Web services.
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Crowdsourcing Based Description of Urban Emergency Events Using Social Media Big Data
TL;DR: In this paper, in order to detect and describe the real time urban emergency event, the 5W (What, Where, When, Who, and Why) model is proposed and results show the accuracy and efficiency of the proposed method.
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Semantic Link Network-Based Model for Organizing Multimedia Big Data
TL;DR: A whole model for generating the association relation between multimedia resources using semantic link network model is proposed, which shows the proposed method can measure the semantic relatedness between Flickr images accurately and robustly.
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Knowle: A semantic link network based system for organizing large scale online news events
TL;DR: In the case study, Knowle is used for organizing and mining health news, which shows the potential on forming the basis of designing and developing big data analytics based innovation framework in the health domain.
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Online Comment-Based Hotel Quality Automatic Assessment Using Improved Fuzzy Comprehensive Evaluation and Fuzzy Cognitive Map
TL;DR: This paper improves fuzzy comprehensive evaluation (FCE) by importing trustworthy degree to it and proposes an automatic hotel service quality assessment method using the improved FCE, which can automatically get more trustworthy evaluation from a large amount of less trustworthy online comments.