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| title | chunk | source | category | tags | date_saved | instance |
|---|---|---|---|---|---|---|
| Expertise finding | 2/2 | https://en.wikipedia.org/wiki/Expertise_finding | reference | science, encyclopedia | 2026-05-05T07:07:58.254989+00:00 | kb-cron |
== Approaches for creating expertise content == Manual, either by experts themselves (e.g., Skillhive) or by a curator (Expertise Finder) Automated, e.g., using software agents (e.g., MIT's ExpertFinder) or a combination of agents and human curation (e.g., ResearchScorecard ) In industrial expertise search engines (e.g., LinkedIn), there are many signals coming into the ranking functions, such as, user-generated content (e.g., profiles), community-generated content (e.g., recommendations and skills endorsements) and personalized signals (e.g., social connections). Moreover, user queries might contain many other aspects rather required expertise, such as, locations, industries or companies. Thus, traditional information retrieval features like text matching are also important. Learning to rank is typically used to combine all of these signals together into a ranking function
== Collaborator discovery == In academia, a related problem is collaborator discovery, where the goal is to suggest suitable collaborators to a researcher. While expertise finding is an asynchronous problem (employer looking for employee), collaborator discovery can be distinguished from expertise finding by helping establishing more symmetric relationships (collaborations). Also, while in expertise finding the task often can be clearly characterized, this is not the case in academic research, where future goals are more fuzzy.
== References ==
== Further reading == Ackerman, Mark and McDonald, David (1998) "Just Talk to Me: A Field Study of Expertise Location" Proceedings of the 1998 ACM Conference on Computer Supported Cooperative Work. Hughes, Gareth and Crowder, Richard (2003) "Experiences in designing highly adaptable expertise finder systems" Proceedings of the DETC Conference 2003. Maybury, M., D'Amore, R., House, D. (2002). "Awareness of organizational expertise." International Journal of Human-Computer Interaction 14(2): 199–217. Maybury, M., D'Amore, R., House, D. (2000). Automating Expert Finding. International Journal of Technology Research Management. 43(6): 12–15. Maybury, M., D'Amore, R, and House, D. December (2001). Expert Finding for Collaborative Virtual Environments. Communications of the ACM 14(12): 55–56. In Ragusa, J. and Bochenek, G. (eds). Special Section on Collaboration Virtual Design Environments. Maybury, M., D'Amore, R. and House, D. (2002). Automated Discovery and Mapping of Expertise. In Ackerman, M., Cohen, A., Pipek, V. and Wulf, V. (eds.). Beyond Knowledge Management: Sharing Expertise. Cambridge: MIT Press. Mattox, D., M. Maybury, et al. (1999). "Enterprise expert and knowledge discovery". Proceedings of the 8th International Conference on Human-Computer Interactions (HCI International 99), Munich, Germany. Tang, J., Zhang J., Yao L., Li J., Zhang L. and Su Z.(2008) "ArnetMiner: extraction and mining of academic social networks" Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. Viavacqua, A. (1999). "Agents for expertise location". Proceedings of the 1999 AAAI Spring Symposium on Intelligent Agents in Cyberspace, Stanford, CA.