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Scientific Referential Metadata Creation with Information Retrieval and Labeled Topic Modeling
| Content Provider | Semantic Scholar |
|---|---|
| Author | Liu, Xiaozhong |
| Copyright Year | 2013 |
| Abstract | The goal of this research is to propose an innovative method of creating scientific referential metadata for a cyberinfrastructure-enabled learning environment to enhance learning experiences and to help students and scholars obtain better understanding of scientific publications. By using information retrieval, topic modeling, and meta-search approaches, different types of resources, such as related Wikipedia Pages, Datasets, Source Code, Video Lectures, Presentation Slides, and (online) Tutorials, for an assortment of publications and scientific (labeled) 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 based on MAP, MRR, and NDCG 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. |
| File Format | PDF HTM / HTML |
| DOI | 10.9776/13192 |
| Alternate Webpage(s) | https://www.ideals.illinois.edu/bitstream/handle/2142/36041/192.pdf?sequence=4 |
| Alternate Webpage(s) | https://doi.org/10.9776/13192 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
