Week 3: Semantic Web, Linked Data, & Intelligent Curriculum

I think its a wonderful project, but perhaps a little too ambitious, and I'll tell you why. Humans love to re-purpose things in unexpected ways, and I cant see a set of tags which would be broad enough to encompass all possible uses, yet retain enough specificity to be useful. As an example:
Once upon a time there was an astronomer who was looking at galaxies, and after two years of looking at galaxies everyday they asked for help:
http://en.wikipedia.org/wiki/Galaxy_Zoo
and currently
http://www.zooniverse.org/projects
Whats interesting in all of this is that:
1) These are collections of big data which still need many thousands of people to generate knowledge, and
2) A specific tool for a specific task, (deciding whether galaxies were spirals or not) is now looking for space junk on the moon, examining solar storms, and reading old ships logs to find out about global warming.
This is a very human thing to do, other examples, a website set up for selling Pez dispensers turned into Ebay, and a piece of software set up to help an academic keep track of references, turned into Google.
This is human.
At our best we look above and beyond.
The second problem is the one alluded to in the old joke about the British and the Americans being two nations divided by a common language.
As a chemist I once studied a set of small molecules each containing 4 atoms.
I was talking to another computational chemist and they recommended a particular piece of software as being good for small molecules.
I tried it, and it failed.
When I met them again, I asked about it. It turned out that they were doing computational biochemistry, and small for them was around 10,000 or so atoms big.
Even in very closely related fields terms can have very different meanings.
My 2c.
Laurence Cuffe

It is indeed true that not everybody finds relevant information in the same way. Indeed there are many students that still miss the skill to come up with relevant tags, screen options that pop-up. The difficulty might be related to understanding what makes meaning or what will deliver personal, relevant results.

Maybe the way students use tags to describe learning objects, and to search through them, can give an idea on how the first steps the semantic web are taken? At first tags and things do not really match sensitive differences in meaning, but as time goes by and linguistic semantics are fine-tuned, the tags linked to object become more precise.

I would think that this precise tagging and finding the most relevant key words related to a certain subject, is linked to the novice or expert state that a person is in. The more expertise, the easier it is to provide specific and accurate key words or phrases, the more one is a novice, the more those choices might be colored by popular nouns or hyped words.

@Laurence: I also really like your idea that human beings have the tendency to go above and beyond. If this urge to move beyond could be put into an algorithm, learning analytics would provide us with cognitive or other laws that we would not even have imagined (or proven).