Abstract
In using a search engine, we propose words as a search query that we believe are likely to locate various kinds of desired information. When a search result fails to include the desired information or does not answer our questions, we often change the search query based on the difference between the contents of the search result and the desired information. Unless the change is effective, we will continue to get unsatisfactory information even if we repeatedly change our search query. In this research, we define a search-query-setting skill, which we define as the ability to effectively change a search query, and propose a system that improves such skills by helping users find effective words as search queries. Our system shows the distributions of search results before and after changing search queries. By comparing them, users can evaluate their changes.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Nguyen, T.N., Zhang, J.: A novel visualization model for web search results. IEEE Trans. Vis. Comput. Graph. 12(5), 981–988 (2006)
Yoshida, T., Oyama, S., Nakamura, S., Tanaka, K.: Query transformation by visualizing and utilizing information about what users are or are not searching. In: Proceedings of the 11th International Conference on Asia-Pacific Digital Libraries (ICADL 2008), vol. 5362, pp. 124–133 (2008)
Ma, C., Zhang, B.: A new query recommendation method supporting exploratory search based on search goal shift graphs. IEEE Trans. Knowl. Data Eng. 30(11), 2024–2036 (2018)
Saito, H., Miwa, K.: Construction of a learning environment supporting learners’ reflection: a case of information seeking on the Web. Comput. Educ. 49(2), 214–229 (2007)
Nishiyama, T., Suwa, M.: Visualization of posture changes for encouraging meta-cognitive exploration of sports skill. Int. J. Comput. Sci. Sport 9(3), 42–52 (2010)
Broder, A.: A taxonomy of Web search. ACM SIGIR Forum 36(2), 3–10 (2002)
Satou, T., Hashimoto, T., Okumura, M.: Implementation of a word segmentation dictionary called mecab-ipadic-NEologd and study on how to use it effectively for information retrieval. In: Proceedings of the Twenty-third Annual Meeting of the Association for Natural Language Processing, pp. 875–878 (2017). (in Japanese)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Mano, C., Kojiri, T. (2021). Cultivation System of Search-Query-Setting Skill by Visualizing Search Results. In: Yamamoto, S., Mori, H. (eds) Human Interface and the Management of Information. Information Presentation and Visualization. HCII 2021. Lecture Notes in Computer Science(), vol 12765. Springer, Cham. https://doi.org/10.1007/978-3-030-78321-1_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-78321-1_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-78320-4
Online ISBN: 978-3-030-78321-1
eBook Packages: Computer ScienceComputer Science (R0)