Authors:
Antoine Flepp
1
;
Julie Dugdale
2
;
Fabrice Bourge
3
and
Tiphaine Marie-Cardot
3
Affiliations:
1
Research Area of Digital Enterprise, Orange Labs, 14000, Caen, France, CNRS – LIG, Univ. Grenoble Alpes, F-38000, Grenoble and France
;
2
CNRS – LIG, Univ. Grenoble Alpes, F-38000, Grenoble and France
;
3
Research Area of Digital Enterprise, Orange Labs, 14000, Caen and France
Keyword(s):
Conversation Threading, Communication, Collaboration, Knowledge, Knowledge Worker, Explicit Knowledge, Tacit Knowledge, Digital Tools, Messaging Service, Digital Communication and Collaboration Tools, Collaborative Conversation of Document Production.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Collaborative Filtering
;
Data Analytics
;
Data Engineering
;
Foundations of Knowledge Discovery in Databases
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Symbolic Systems
Abstract:
Many companies have a suite of digital tools, such as Enterprise Social Networks, conferencing and document sharing software, and email, to facilitate collaboration among employees. During, or at the end of a collaboration, documents are often produced. People who were not involved in the initial collaboration often have difficulties understanding parts of its content because they are lacking the overall context. We argue there is valuable contextual and collaborative knowledge contained in these tools (content and use) that can be used to understand the document. Our goal is to rebuild the conversations that took place over a messaging service and their links with a digital conferencing tool during document production. The novelty in our approach is to combine several conversation-threading methods to identify interesting links between distinct conversations. Specifically we combine header-field information with social, temporal and semantic proximities. Our findings suggest the mes
saging service and conferencing tool are used in a complementary way. The primary results confirm that combining different conversation threading approaches is efficient to detect and construct conversation threads from distinct digital conversations concerning the same document.
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