%0 Conference Paper %1 conf/sigir/XuF0N18 %A Xu, Luyan %A Fernando, Zeon Trevor %A Zhou, Xuan %A Nejdl, Wolfgang %B Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval %D 2018 %E Collins-Thompson, Kevyn %E Mei, Qiaozhu %E Davison, Brian D. %E Liu, Yiqun %E Yilmaz, Emine %I ACM %K myown %P 1289-1292 %R 10.1145/3209978.3210169 %T LogCanvas: Visualizing Search History Using Knowledge Graphs %U https://doi.org/10.1145/3209978.3210169 %X In this demo paper, we introduce LogCanvas, a platform for user search history visualisation. Different from the existing visualisation tools, LogCanvas focuses on helping users re-construct the semantic relationship among their search activities. LogCanvas segments a user’s search history into different sessions and generates a knowledge graph to represent the information exploration process in each session. A knowledge graph is composed of the most important concepts or entities discovered by each search query as well as their relationships. It thus captures the semantic relationship among the queries. LogCanvas offers a session timeline viewer and a snippets viewer to enable users to re-find their previous search results efficiently. LogCanvas also provides a collaborative perspective to support a group of users in sharing search results and experience.

@inproceedings{conf/sigir/XuF0N18, abstract = {In this demo paper, we introduce LogCanvas, a platform for user search history visualisation. Different from the existing visualisation tools, LogCanvas focuses on helping users re-construct the semantic relationship among their search activities. LogCanvas segments a user’s search history into different sessions and generates a knowledge graph to represent the information exploration process in each session. A knowledge graph is composed of the most important concepts or entities discovered by each search query as well as their relationships. It thus captures the semantic relationship among the queries. LogCanvas offers a session timeline viewer and a snippets viewer to enable users to re-find their previous search results efficiently. LogCanvas also provides a collaborative perspective to support a group of users in sharing search results and experience.}, added-at = {2018-10-02T06:38:42.000+0200}, author = {Xu, Luyan and Fernando, Zeon Trevor and Zhou, Xuan and Nejdl, Wolfgang}, biburl = {https://www.bibsonomy.org/bibtex/293dba809d2379debfa8c649d9f1b80e3/alexandriaproj}, booktitle = {Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval}, crossref = {conf/sigir/2018}, doi = {10.1145/3209978.3210169}, editor = {Collins-Thompson, Kevyn and Mei, Qiaozhu and Davison, Brian D. and Liu, Yiqun and Yilmaz, Emine}, ee = {http://doi.acm.org/10.1145/3209978.3210169}, eventdate = {July 08 - 12, 2018}, eventtitle = {SIGIR '18}, interhash = {7e7f8fb86e535a244e6e2dd66eeae668}, intrahash = {93dba809d2379debfa8c649d9f1b80e3}, keywords = {myown}, month = jul, pages = {1289-1292}, publisher = {ACM}, timestamp = {2018-10-02T06:38:42.000+0200}, title = {LogCanvas: Visualizing Search History Using Knowledge Graphs}, url = {https://doi.org/10.1145/3209978.3210169}, venue = {Ann Arbor, MI, USA}, year = 2018 }