What is the K in KM? I in IR?

What is the K in KM (Knowledge Management)? What is managed in KM? Can we really manage knowledge?

In other words, how is KM different from information management?

Before trying to answer these questions, we might as well ask what is the I in IR (Information Retrieval)? When have you really retrieved information from a search engine? I seem to always retrieve data, text, or occasionally images or audio/video.

The data or text that is retrieved is then processed in our brains to extract information from it. If it is data that is retrieved, we are talking about good old query processing in databases. If it is text or other multimedia, that is content retrieval, not quite information retrieval.

How come then we are comfortable talking about the I in IR being information? And why are people more skeptical of the K in KM being knowledge?

Well, there are a few subtle but important differences between IR and KM. In IR, we are more comfortable with the notion of extracting information from the retrieved data or text. We are not so sure of the meaning of extracting knowledge from the same stuff.
Next, IR emphasizes retrieval, a smaller problem than all of information management. KM is being more ambitious in talking about managing knowledge. Also, for IR, it seems alright to believe that managing reasonable kinds of meta data (that librarians have been using for ages) enables the engine to find relevant data and text. What needs to be represented for KM? And how is the meta data in KM different from that in IR?

To see the additional kinds of meta data that may be needed for KM, consider the following types of questions that a librarian may get: "Who has written books on database management?" is a simple data retrieval question. "Which book on databases covers database tuning?" is an IR question. And "Which database book explains tuning well?" is really a KM question. For the first one, a simple database schema will help us translate the question into an appropriate database query (in SQL, for instance). For the IR question, we can come up with a reasonable schema for storing the tables of contents of books (or keyword or other classification data) to meet the requirements. For the KM query, what needs to be represented and managed so that the system can pick books that explain tuning better than others?

The K in KM is not really knowledge itself, but a representation of it. This knowledge representation is mostly meta data but it is rich with "knowledge level attributes."

What are knowledge attributes? In my next posting, if you don't mind ...