Finding relevant publications is a common task. Typically, a researcher browses through a list of publications and traces additional relevant publications. When relevant publications are identified, the list may be expanded by the citation links of the relevant publications. The information needs of researchers may change as they go through such iterative processes. The exploration process quickly becomes cumbersome as the list expands. Most existing academic search systems tend to be limited in terms of the extent to which searchers can adapt their search as they proceed. In this article, we introduce an adaptive visual exploration system named PaperPoles to support exploration of scientific publications in a context-aware environment. Searchers can express their information needs by intuitively formulating positive and negative queries. The search results are grouped and displayed in a cluster view, which shows aspects and relevance patterns of the results to support navigation and exploration. We conducted an experiment to compare PaperPoles with a list-based interface in performing two academic search tasks with different complexity. The results show that PaperPoles can improve the accuracy of searching for the simple and complex tasks. It can also reduce the completion time of searching and improve exploration effectiveness in the complex task. PaperPoles demonstrates a potentially effective workflow for adaptive visual search of complex information.
|Number of pages||15|
|Journal||Journal of the Association for Information Science and Technology|
|Publication status||Published - 2019 Aug|
Bibliographical noteFunding Information:
Jiangen He, Qing Ping, and Chaomei Chen thank the National Science Foundation (Award Number: 1633286) for support. Wen Lou thanks the National Social Science Foundation of China (Award Number: 17CTQ025) for support.
Jiangen He, Qing Ping, and Chaomei Chen thank the National Science Foundation (Award Number: 1633286) for support. Wen Lou thanks the National Social Science Foundation of China (Award Number: 17CTQ025) for support. The authors thank Andrea Forte, Swathi Jagannath, Ali Jazayeri, Adam Johs, Kai Li, Meaghan Lutts, and Wei Quan at Drexel University for suggestions and comments on the study and writing, and also thank Changyang Feng, Xin Li, and Ye Chen at Wuhan University for their help in the participant recruitment. We also appreciate the constructive comments from the anonymous reviewers.
© 2019 ASIS&T
All Science Journal Classification (ASJC) codes
- Information Systems
- Computer Networks and Communications
- Information Systems and Management
- Library and Information Sciences