I met with some officials from the Department of Defense's National Security Agency today. I was describing our work on commonsense reasoning when the question came up of what did we mean by the word "knowledge." In our systems we usually start off with a corpus of commonsense facts, stories, descriptions, etc. expressed in english, but which are then converted through a variety of processing techniques into more usable knowledge representations such as semantic networks and probabilistic graphical models. My suggestion was that, from the perspective of the computer, only the latter forms should be considered "knowledge" because they could be put to use by an automated inference procedure. But in the long run, as our parsing and reasoning tools get more sophisticated, we may come to be able to use more and more of the collected corpus.
Generally, the word "knowledge" is very inclusive because the AI community has discovered a vast array of knowledge representation forms, and every one of them is useful for some purpose or the other. Thus, the more important questions may not be what is and isn't knowledge, but given some knowledge, questions such as the following:
For what purposes can it be used? When is it applicable? Is it true? According to who? Under what circumstances? Who might find this knowledge useful? Is it expressed clearly enough? Are there other units of knowledge that may be useful in conjunction with this one? How long should we expect this knowledge to stay relevant? How might have this knowledge been acquired, and from where might we acquire more like it? What background might you need to make sense of it?
And so forth. The point, I suppose, is that like most words that point to complex ideas, understanding the word "knowledge" requires that we consider its many contexts of use, and the issues that show up in those contexts.