Abstract
Lengthy lists of search results are the fruit of both short queries and conventional Web search result displays. They are problematic for meeting user's information needs. This paper describes the first part, topic extraction and representation from metadata, of a project that will develop an interactive visual query refinement and recommendation (QRR) service to alleviate the problems due to lengthy lists of search results. The topic extraction uses the Latent Dirichlet Allocation (LDA) algorithm to mine the intra- and inter-document relations and represent them in topic and features. The paper presents how the LDA algorithm extracts topics and features from metadata records contained in NSDL search results, which will be used by an interactive QRR service in the next step of the project.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 15-20 |
| Number of pages | 6 |
| Journal | Proceedings of the International Conference on Dublin Core and Metadata Applications |
| State | Published - 2009 |
| Event | 9th International Conference on Dublin Core and Metadata Applications, DC-2009 - Seoul, Korea, Republic of Duration: Oct 12 2009 → Oct 16 2009 |
Keywords
- LDA algorithm
- Query refinement
- Query refinement and recommendation (QRR)
- Topic detection and tracking
ASJC Scopus subject areas
- Computer Science Applications
- Computer Vision and Pattern Recognition
- Software