Background and objective: Online communities of practice contain a wealth of information, stored in the free text of shared communications between community members. The Knowledge Maps (KMaps) system is designed to facilitate Knowledge Translation in online communities through multi-level analyses of the shared messages of these communications.
Methods: Using state-of-the-art semantic mapping technologies (Metamap) the contents of the messages shared within an online community are mapped to terms from the MeSH medical lexicon, providing a multi-level topic-specific summary of the knowledge being shared within the community. Using the inherent hierarchical structure of the lexicon important insights can be found within the community.
Results: The KMaps system was applied to two medical mailing lists, the PPML (archives from 2009-02 to 2013-02) and SURGINET (archives from 2012-01 to 2013-04), identifying 27,924 and 50,597 medical terms respectively. KMaps identified content areas where both communities found interest, specifically around Diseases, 22% and 24% of the total terms, while also identifying field-specific areas that were more popular: SURGINET expressed an interest in Anatomy (14% vs 4%) while the PPML was more interested in Drugs (19% vs 9%). At the level of the individual KMaps identified 6 PPML users and 9 SURGINET users that had noticeably more contributions to the community than their peers, and investigated their personal areas of interest.
Conclusion: The KMaps system provides valuable insights into the structure of both communities, identifying topics of interest/shared content areas and defining content-profiles for individual community members. The system provides a valuable addition to the online KT process.
Keywords: Knowledge management; Medical taxonomies; Natural language processing; Online communities; Semantic mapping.
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