报告人：刘晓钟 副教授（美国Indiana University Bloomington， IUB）
讲座题目：Citation + Full-text Scholarly Information Analysis
讲座内容简介：The sheer volume of scholarly publications available online significantly challenges how scholars retrieve the new information available and locate the candidate reference papers. While classical text retrieval and graph mining algorithms can assist scholars in accessing needed publications, in this study, I propose an innovative publication ranking method by leveraging citation plus full-text scholarly information on the heterogeneous bibliographic graph. Different kinds of information on the graph address different ranking hypotheses, whereas the pseudo-relevant papers/topics can be used to assess the importance of each kind of scholarly objects, e.g., papers, authors, venues and topics.
报告人简介： Dr. Xiaozhong Liu is an Associate Professor at School of Informatics and Computing, Indiana University Bloomington. His research interests include metadata, information retrieval, natural language processing, text mining, knowledge management, and human computing. His dissertation at Syracuse University explored an innovative ranking method that weighted the retrieved results by leveraging dynamic community interests. In contrast to most existing studies in scientific resource recommendation, his research developed an enhanced understanding of the scholarly network from a topical content perspective and investigated the use of full-text citation data to improve the overall recommendation ranking performance. He proposed SWALE and Collaborative PDF reader projects, which will generate innovative metadata along with next generation knowledge retrieval and question answering systems.