This area encompasses Business Rules, Content Management and Document Management. The business rules approach is primarily finding the rules in such unstructured information, although occasionally business rules also aim to extract similar knowledge from legacy transaction systems. The central idea is that a knowledge engineer, especially coupled with a domain expert, can read great volumes of documentation and organize that documentation in a way that a system could look up what law, rule, regulation, or bit of corporate lore is important at the point of execution, when another member of the organization could use that knowledge if they had it available.
The semantic angle here is twofold. The first is that until we have some semantic consistency and rigor in this process, the product of the knowledge engineer's work will be idiosyncratic. That is, each knowledge engineer will tag things with keywords or terms or whatever they feel are the important ways to index this information at the time they researched it. Furthermore, two terms which are closely related but have different keywords have no way easy way of being cross-associated. So one of the first things to do in an area like this is to develop a rich ontology, which not only will put some structure to the terms to be used, but also indicate which terms are related to other terms.
The second design of this KM System is if the knowledge engineer uses the same or a compatible ontology to that which is being used in the transactional systems, there is a hope for marrying these two systems. There is a great deal of benefit to be gained in doing this. Imagine transactional systems that had tags for hazardous material in their inventory system. That material would be cross-linked to the knowledge management base of what needs to be done: constraints for transporting, storage, reporting, and the like. This is just one tiny example but I believe you can see the point.
The Semantic Web will bring structure to the meaningful content of Web pages, creating an environment where software agents roaming from page to page can readily carry out sophisticated tasks for users. Such an agent coming to the clinic's Web page will know not just that the page has keywords such as "treatment, medicine, physical, therapy" (as might be encoded today) but also that Dr. Hartman works at this clinic on Mondays, Wednesdays and Fridays and that the script takes a date range in yyyy-mm-dd format and returns appointment times. And it will "know" all this without needing artificial intelligence on the scale of 2001's Hal or Star Wars's C-3PO. Instead these semantics were encoded into the Web page when the clinic's office manager (who never took Comp Sci 101) massaged it into shape using off-the-shelf software for writing Semantic Web pages along with resources listed on the Physical Therapy Association's site.