SLIS faculty member Dr. Xiaozhong Liu and students Jinsong Zhang (visiting PhD Student), Chun Guo (MIS), and Han Jia (MIS) presented two posters at the ACM/IEEE Joint Conference on Digital Libraries that took place in Washington, DC on June 10-12, 2012. Dr. Liu is a member of Jinsong Zhang's doctoral dissertation committee. Chun Guo has been admitted to the SLIS Ph.D. in Information Science starting the Fall 2012 semester. And, Han Jia is a student in the SLIS Master of Information Science degree program.
The poster by Jinsong Zhang, Chun Guo, and Professor Liu was entitled “Characterize Scientific Domain and Domain Context.” The poster by Professor Liu and Han Jia was entitled “Scientific Cyberlearning Resources Referential Metadata.” The abstracts are listed below.
• Jinsong Zhang, Chun Guo and Xiaozhong Liu (2012) Characterize Scientific Domain and Domain Context. ACM/IEEE Joint Conference on Digital Libraries (poster)
Domain knowledge map construction as an important method can describe the significant characters of a selected domain. In this research, we will address three problems for knowledge graph generation. Firstly, this paper will construct domain (core journals and conference proceedings) knowledge and domain context (domain citation) knowledge graphs, and propose a novel method to integrate those graphs. Secondly, two different methods will be investigated to associate keywords on the graph: Co-occur Domain Distance and Citation Probability Distribution Distance. Last but not least, the paper will propose an innovative method to evaluate the accuracy and coverage of knowledge graphs based on training keyword oriented Labeled-LDA model and validate different domain or domain context graphs.
• Xiaozhong Liu and Han Jia (2012) Scientific Cyberlearning Resources Referential Metadata. ACM/IEEE Joint Conference on Digital Libraries (poster)
The goal of this research is to describe an innovative method of creating scientific referential metadata for a cyberinfrastructure enabled learning environment to enhance student and scholar learning experiences. By using information retrieval and metasearch approaches, different types of referential metadata, such as related Wikipedia Pages, Datasets, Source Code, Video Lectures, Presentation Slides, and (online) Tutorials, for an assortment of publications and scientific topics will be automatically retrieved, associated, and ranked. In order to test our method of automatic cyberlearning referential metadata generation, we designed a user experiment for the quality of the metadata for each scientific keyword and publication and resource ranking algorithms. Evaluation results show that the cyberlearning referential metadata retrieved via meta-search and statistical relevance ranking can effectively help students better understand the essence of scientific keywords and publications.
Posted June 25, 2012