Details
| Original language | English |
|---|---|
| Title of host publication | Advances in Web-Based Learning |
| Subtitle of host publication | ICWL 2019 - 18th International Conference, 2019, Proceedings |
| Editors | Michael A. Herzog, Zuzana Kubincová, Peng Han, Marco Temperini |
| Pages | 229-240 |
| Number of pages | 12 |
| Edition | 1. |
| ISBN (electronic) | 978-3-030-35758-0 |
| Publication status | Published - 16 Nov 2019 |
| Event | 18th International Conference on Advances in Web-Based Learning, ICWL 2019 - Magdeburg, Germany Duration: 23 Sept 2019 → 25 Sept 2019 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 11841 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
Search history visualization provides a medium to organize and quickly re-find information in searching. Scientific studies show that a good visualization of a user search history should not only present the explicit activities represented by search queries and answers but also depict the latent information exploration process in the searcher’s mind. In this paper, we propose the LogCanvasTag platform for search history visualization. In comparison to existing work, we focus more on helping searchers to re-construct the semantic relationship among their search activities. We segment a user’s search history into different sessions and use a knowledge graph to represent the searching process in each of the sessions. The knowledge graph consists of all queries and important related concepts as well as their relationships and the topics extracted from the search results of each query. Especially to help searchers not get lost in complicated history graph, we provide a function wherein sub-graphs can be extracted for each topic from the session graph for deeper insights. We also provide a collaborative perspective to support a group of users in sharing search activities and experience. Our experimental results indicate that searching experience of both independent users and collaborative searching groups benefit from this search history visualization. We present novel insights into the factors of graph-based search history visualization that help in quick information re-finding.
Keywords
- Collaborative search, Information re-finding, Search history visualization
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
Cite this
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Advances in Web-Based Learning: ICWL 2019 - 18th International Conference, 2019, Proceedings. ed. / Michael A. Herzog; Zuzana Kubincová; Peng Han; Marco Temperini. 1. ed. 2019. p. 229-240 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11841 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Visualizing Search History in Web Learning
AU - Tolmachova, Tetiana
AU - Xu, Luyan
AU - Marenzi, Ivana
AU - Gadiraju, Ujwal
PY - 2019/11/16
Y1 - 2019/11/16
N2 - Search history visualization provides a medium to organize and quickly re-find information in searching. Scientific studies show that a good visualization of a user search history should not only present the explicit activities represented by search queries and answers but also depict the latent information exploration process in the searcher’s mind. In this paper, we propose the LogCanvasTag platform for search history visualization. In comparison to existing work, we focus more on helping searchers to re-construct the semantic relationship among their search activities. We segment a user’s search history into different sessions and use a knowledge graph to represent the searching process in each of the sessions. The knowledge graph consists of all queries and important related concepts as well as their relationships and the topics extracted from the search results of each query. Especially to help searchers not get lost in complicated history graph, we provide a function wherein sub-graphs can be extracted for each topic from the session graph for deeper insights. We also provide a collaborative perspective to support a group of users in sharing search activities and experience. Our experimental results indicate that searching experience of both independent users and collaborative searching groups benefit from this search history visualization. We present novel insights into the factors of graph-based search history visualization that help in quick information re-finding.
AB - Search history visualization provides a medium to organize and quickly re-find information in searching. Scientific studies show that a good visualization of a user search history should not only present the explicit activities represented by search queries and answers but also depict the latent information exploration process in the searcher’s mind. In this paper, we propose the LogCanvasTag platform for search history visualization. In comparison to existing work, we focus more on helping searchers to re-construct the semantic relationship among their search activities. We segment a user’s search history into different sessions and use a knowledge graph to represent the searching process in each of the sessions. The knowledge graph consists of all queries and important related concepts as well as their relationships and the topics extracted from the search results of each query. Especially to help searchers not get lost in complicated history graph, we provide a function wherein sub-graphs can be extracted for each topic from the session graph for deeper insights. We also provide a collaborative perspective to support a group of users in sharing search activities and experience. Our experimental results indicate that searching experience of both independent users and collaborative searching groups benefit from this search history visualization. We present novel insights into the factors of graph-based search history visualization that help in quick information re-finding.
KW - Collaborative search
KW - Information re-finding
KW - Search history visualization
UR - http://www.scopus.com/inward/record.url?scp=85076761299&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-35758-0_21
DO - 10.1007/978-3-030-35758-0_21
M3 - Conference contribution
AN - SCOPUS:85076761299
SN - 978-3-030-35757-3
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 229
EP - 240
BT - Advances in Web-Based Learning
A2 - Herzog, Michael A.
A2 - Kubincová, Zuzana
A2 - Han, Peng
A2 - Temperini, Marco
T2 - 18th International Conference on Advances in Web-Based Learning, ICWL 2019
Y2 - 23 September 2019 through 25 September 2019
ER -