I agree that using "intelligent text mining" is an interesting approach to expertise location in companies (and on the internet). We experimented with this some time ago in the company I work for with interesting results. This experiment was set up because - as we all experience - employees fill in their Yellow Page profile, but don't keep them up to date. (In our company 10% filled in their profile and 3% of that 10% kept it up-to-date...) Relating the filled-in profile to mining could trigger employees to keep it up to date. And it could also (partially) fill in their profile.
We also combined this with a more social approach, which is now being capitalized in Guruscan. Because using mining to find and define expertise limits you to what's in databases. And when we write reports about a tool, for instance, we don't mention we're very good at PERL programming. Maybe the report shortly mentioned the tool has been programmed in PERL, but that doesn't say much about the level of expertise. So, this social layer collects the tacit stuff. It's a necessary layer and it's also closet to real expert networks.
We've published quite a bit on our work. Here are two references:
- Samuel Driessen, Willem-Olaf Huijsen, Marjan Grootveld, “A framework for evaluating knowledge-mapping tools”, Journal of Knowledge Management, 2007, Vol. 11, Iss. 2, page 109-117.
- Willem-Olaf Huijsen, Samuël J. Driessen, Dion Slijp, "ExpertFinder: Collaborative Expertise Localization", I-Media 2007.