Tagging and the Semantic Web

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Originally published at Internet.com

A while back I commented on a TechCrunch article quoting Twine CEO Nova Spivack regarding keyword searches in the Semantic Web space. My comment was later quoted on the Faviki blog, a semantic startup involving tagging web pages with semantic Wikipedia data. I thought it would be useful here to go into a little bit more depth on semantic tagging and what weve learned thus far.

Tags the way they are implemented today

The way the better Web 2.0 sites implement tags involves faceting. In a nutshell, it allows you to group together documents or objects based on attributes. For example, a collection of all documents about George Bush and Washington. The problem with these attributes is they have little or no value on their own and they certainly are not understood by computers. They are just strings denoting some type of concept. To that end, here is a short list of limitations that the Semantic Web will address:

Tags do not provide enough meaningful metadata to make meaningful comparisons.

More information is needed besides their origin.

Tags are essentially a full text search mechanism, although faceting helps.

Need more relationships between tags and the objects they pertain to.

The solution: Tags as objects

Allowing users to tag an object with another object allows us to make extremely interesting comparisons; discerning a lot more information about the original object becomes simple and accurate. With this type of interrelationship we can pivot through the data like never before, not with full text search but with object graph linkages that machines and humans can understand.

Lets go over an example:

Lets say a user adds a note into our system ranting about a beet farmer who lives in Washington state by the name of William Gates. The user goes on to discuss his beets and farming techniques in great detail, mentioning nothing about software and Windows Vista, of course. In the current Internet model the user would tag this note with strings like, William Gates, Bill Gates, beets, etc.

Now another user comes along and starts digging through documents tagged Bill Gates to try and find new articles about Vista. Unfortunately, many searches will turn up bad results, especially if the density of the word Bill Gates is great enough in the document about beets. That being said, the other direction would work more as intended, searching on the tags Bill Gates and Beets would yield more expected results.

In the Semantic Web model, the document about William Gates (the beet farmer) would be tagged with the William Gates object that could contain a plethora of metadata, including his location, occupation, etc. Now when we look at this document there is no guessing as to what it is referring, especially from a machines point of view. This is exactly what the Semantic Web was built for. In this model we are not relying on linguistics, natural language processing, or full text search. We are relying on hard links that machines can understand and relate to.

The disambiguation page (was the tag page in Web 2.0)

What about regular string tags? The thought is that the Semantic Web cant possibly understand everything and the fact of the matter is, its true. As a result with Twine we still support regular string tagging. Some things are not proper nouns and less concrete, like adjectives and verbs. They may not yet deserve their own object. However, before we throw in the towel, lets think about actual language here for a second, i.e. the semantics behind how we describe things.

Take the adjective cool. Well, first of all, what are you looking for? Nouns? A grouping of multiple nouns? Probably cool nouns, in fact. A search on this tag could turn up anything and everything from many different levels. It could start by pulling in a definition from Wikipedia. Then it could group together a list of groups tagged cool like the Super Cars group or the Fast Cars group. It would also show you what users tagged cool and documents have been tagged cool. But it becomes really interesting is where you find the cool string tag on a tag object. Now you can find proper noun tags like Ferrari as well as Super Cars - the proper noun.

Joining these tags together in a search would yield detailed results from rich metadata, like a list of Ferraris over the years, represented as objects. Each car object would contain detailed specs on engine type, weight, horsepower, etc. Then by examining the Ferrari Enzo object we can find all the people who used this tag on their bookmarks, links, documents, or other objects they created. With this information you can connect with these people, join their groups, and further your search for whatever it is you are interested in. The point here is that everything is related at many different levels. What links them together are the adjectives and verbs that describe them.

Semantic Conclusions

To be able to come at your data from every angle is important. Everyone thinks differently and therefore everyone searches differently. The truth is, it is going to be quite a while before machines really start to understand what we humans are talking about. It is up to us to help organize data in a format that is machine readable so the machines can share, but in return it allows us to perform incredible searches likes never before. One day all this work will pay off and the machines may be able comprehend what the word cool means; until then it us up to us to think for ourselves!