Spatio-temporal pseudo relevance feedback for scientific data retrieval

We consider the problem of searching scientific data from vast heterogeneous scientific data repositories. This problem is challenging because scientific data contain relatively little text information compared to other search targets such as web pages. On the other hand, the metadata in scientific data contain other characteristic information such as spatio-temporal information. Although using this information make it possible to improve the search performance, many widely adopted scientific data search engines use this information exclusively for narrowing down search results. In this paper, we propose a novel query generation method using spatial, temporal, and text information based on pseudo relevance feedback. The proposed method generates new spatio-temporal queries from the initial search results. By using these queries, the search results are reranked such that more related results obtain higher rank. The experimental results show that the proposed method outperforms a baseline method when search targets do not have rich text information.