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Nowadays communication exchanges are an integral and time consuming part of people's job, especially for the so called knowledge workers. Contents discussed during meetings, instant messaging exchanges, email exchanges therefore constitute a potential source of knowledge within an organisation, which is only shared with those immediately involved in the particular communication act. This poses a knowledge management issue, as this kind of contents become "buried knowledge". This work uses semantic technologies to extract buried knowledge, enabling expertise finding and topic trends spotting. Specifically we claim it is possible to automatically model people's expertise by monitoring informal communication exchanges (email) and semantically annotating their content to derive dynamic user profiles. Profiles are then used to calculate similarity between people and plot semantic knowledge-based networks. The major contribution and novelty of this work is the exploitation of semantic concepts captured from informal content to build a semantic network which reflects people expertise rather than capturing social interactions. We validate the approach using contents from a research group internal mailing list, using email exchanges within the group collected over a ten months period.

Extracting Semantic User Networks from Informal Communication Exchanges