Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/49963

Enhancing Scientific Collaboration Through Knowledge Base Population and Linking for Meetings

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dc.contributor.author Gao, Ning
dc.contributor.author Dredze, Mark
dc.contributor.author Oard, Douglas
dc.date.accessioned 2017-12-28T00:38:29Z
dc.date.available 2017-12-28T00:38:29Z
dc.date.issued 2018-01-03
dc.identifier.isbn 978-0-9981331-1-9
dc.identifier.uri http://hdl.handle.net/10125/49963
dc.description.abstract Recent research on scientific collaboration shows that distributed interdisciplinary collaborations report comparatively poor outcomes, and the inefficiency of the coordination mechanisms is partially responsible for the problems. To improve in-formation sharing between past collaborators and future team members, or reuse of collaboration records from one project by future researchers, this pa-per describes systems that automatically construct a knowledge base of the meetings from the calendars of participants, and that then link reference to those meetings found in email messages to the correspond-ing meeting in the knowledge base. This is work in progress in which experiments with a publicly avail-able corporate email collection with calendar entries show that the knowledge base population function achieves high precision (0.98, meaning that almost all knowledge base entities are actually meetings) and that the accuracy of the linking from email messages to knowledge base entries (0.90) is already quite good.
dc.format.extent 10 pages
dc.language.iso eng
dc.relation.ispartof Proceedings of the 51st Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Text Mining in Big Data Analytics
dc.subject Avocado email collection, meeting linking, Scientific collaboration
dc.title Enhancing Scientific Collaboration Through Knowledge Base Population and Linking for Meetings
dc.type Conference Paper
dc.type.dcmi Text
dc.identifier.doi 10.24251/HICSS.2018.076
Appears in Collections: Text Mining in Big Data Analytics


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