ChrisScott.org

Whoa, there. Web 5.0?

Okay, so I made up web 5.0. Actually, I detest the numbered generations we’ve applied to the web. The main problem I have with these terms is that they imply a linear progression. They suggest that we are going to abandon the interactive web, Web 2.0, for the semantic web, Web 3.0. Obviously we aren’t. I doubt anyone would even suggest it. Web developers will continue to use both. Hence Web 5.0 (do the maths).

I’m going to drop the term now – it was just a joke. The modern World Wide Web is, in fact, much more than just the three so-called generations – although clearly they are very important. I can identify three main concepts (not technologies) which are facilitating the current evolution of the web:

• Interactivity (2.0)
• Semantic understanding (3.0)
• Commoditization (the Cloud)

Nothing ground breaking there. And we, as users, are certainly seeing more and more of these big three in our daily use of the web.

Interactivity is fairly obvious. I think the biggest revolution in interactive content came about as Wikipedia took off. Undoubtedly the most expansive (centralized) base of knowledge the world has ever seen – and written by volunteers, members of the public. It really is a staggering collaborative achievement. Then there’s blogging, micro-blogging, social networking, professional networking, content discovery (digg, etc), pretty much anything you might want to contribute, you can.

Semantic understanding is a little trickier to see. That’s hardly suprising as it is so much newer and far less understood. Believe the hype, though. The sematic web is coming and it will change everything (everything web related, that is). If you don’t believe me try googling for “net income IBM”. You should see something like this:

That top result is special. It’s special because it’s the answer; it’s what you were looking for. No need to trawl through ten irrelevant pages to find the data – it’s just there. Google managed to display this data because IBM published it as part of an RDF document. If you search for the same information about Amazon – who don’t, no such luck. (That particular example was given by Ellis Mannoia in a great Web 3.0 talk at Internet World this week – so thanks Ellis.)

That leaves us with commoditization. Specifically, the commoditization of functionality from a developers point of view. This concept is largely, although not exclusively, linked to the Cloud. The term “the Cloud” is used broadly to describe services make avalible over the internet. GMail, for example, is email functionality in the cloud. Users don’t need to install anything to use GMail (bar a web client) they just use it when they want, from any computer. Many of the Cloud services out there are available as APIs, and that leads to the commoditization of functionality. Say I want to add a mapping application to my web site to show my audience where I am. A few years ago that would have been a significant amount of development work. These days it’s trivial – you just make a call to the GoogleMaps API. And so map functionalities become a commodity.

The point of this post, however, is that these are not mutually exclusive concepts. There is no reason why you cannot combine semantic understanding with Cloud computing, or UGC, or both. Quite the opposite: combining the three should be the goal.

There are problems, however. Utilizing Cloud computing requires a certain amount of adherence to standards – fitting in to an API. And semantic understanding (and meta data, in general) takes time to accrue. In general those two constraints don’t work well with Web 2.0 functionality.

Let me give an example: If a user contributes a comment to an article they probably won’t take the time to add the meta data required for semantic understanding to be achieved. In the same way if they don’t give their location you can’t show them as a pin on GoogleMaps.

However semantic understanding is (IMHO) more than just the use of RDF documents. Tools like Nstein’s Text Mining Engine can be used to create a semantic footprint describing a piece of text. I’ve talked, in previous posts, about using the data gleaned by the TME in imaginative and experimental ways. Take the example above. If a user were to post a comment about a talk they attended the TME could extract, not only the concepts of the comment, but also data like the location of the subject. That semantic understanding can be used to programatically call the GoogleMaps API to add a new pin in your map.

And there you have it. Semantic understanding of interactive content used to harness the power of Cloud computing. One of the most important benefits of the TME, for me, is the flexibility it affords you. If you know that you can get access to that kind on information it opens up all kinds of possibilities. Exploring some of these possibilities has to be the focus for making a brand stand out against the plethora of content suppliers and aggregators available; for improving the users experience and gaining their loyalty.

So it’s time to stop thinking about Web 2.0 or Web 3.0 and start thinking about the technology and techniques available and how they can be used to the greatest effect.

Well, that might be a bit extreme. At least if they do they should put in a bit more effort.

Perhaps I need to explain my problem here. The complaint I have concerns automatic tweets – popular with bloggers and online publshers in general. Extremely unpersonal, often unhelpful clipits drawing the audiences attention to a new article or blog entry. Here’s an example:

[news] Pepsi drinkers join the dots: Anyone buying a Pepsi Max soft drink over the next few w.. http://tinyurl.com/5qu3w3

- @guardianmedia

Ok, so it’s pretty obvious what’s wrong with this tweet. The article the Guardian Media is trying to promote is about a campaign by Pepsi which uses QR codes on the side of their cans – not that you’d have known from the tweet.

The problem is they’ve used a witty headline not a descriptive one. In itself that is fine. Like many online publishers, however, the Guardian have opted against manually tweeting and have integrated (presumably) their CMS with Twitter. More specifically, the tweet is a concatination of the articles title and the begining of the text. It just so happens that neither of those blocks of text mension QR codes.

There is a lot to be said for automation, though. It’s not just that this system saves the author of the article or blog time. It also ensures consistency – all articles get posted. And, to be fair, most of the time these posts are okay…

…not always though. Personally, I’ve stopped following the Guardian Media on twitter (and Scientific American) because these badly formed tweets annoy me way too much. Take the article above, for example. A human author might tweet something like this:

Pepsi launch campaign using QR codes on cans. Drinkers get access to secret content through phone browser.

That sums up the article much better, with 33 characters spare for the URL. I’d be far more likely to read the article having read that tweet, as I think QR codes are interesting (I’m a bit of a geek) and appreciate imaginative marketing.

So what’s the answer? Is there a way to achieve the normalization and efficiency of an automated system while being a good Twitterer? Well yes, I think there is.

I’ve been playing with the workflow engine in Nstein’s WCM and have written a nifty little Twitter-bot. It’s secret is it’s ability to understand content. Nstein also produce a text mining engine (TME) which is ingrained into the WCM right down to the core. This means that semantic data about an article is always easily accessible. I’ve used this automatically extracted meta data in two ways for my bot.

Firstly, I’ve made use of the TME’s concept and entity extraction features to create hash-tags. For those who don’t know, a hash-tag is a peice of meta-data associated to a tweet. They are prefixed with a hash (#) character and generally are alpha numeric. A lot of automated tweets now use hash-tags with vary degrees of success. @northamptonrfc (the rugby team I support), for example, tags all tweets with “#rugby”. Well I never. The correct use of hash-tags (IMHO) is to:

1. Add relevant meta data to a tweet which adds meaning.
2. Create a trend to follow (essencially a thread accross all Twitter users).

In order to meet those criteria the tag needs to be meaningful. It stands to reason. In the Pepsi example above two tags spring to mind: “#pepsi” and “#qrcode”. Including 2 spaces that makes an extra 15 characters which can (relatively) easily be fitted in before the TinyURL. Nstein’s TME would, undoubtedly, have picked these concepts out.

“QR Code” is what the TME refers to as a complex concept, that is, a phrase. “Pepsi” is an entity, specifically an organisation name. A simple regex can transform these strings into hash-tags. Using this technique the bot imediately adds a great deal of meaning to the tweet.

The second way in which I’ve leveraged the meta data extracted by the TME is using NSummarizer. This cartridge takes a document, splits it into sentence components, rates each component on its relevance to the article and returns the best scoring one(s) as a brief summary of the document. This is a really useful tool for getting around the issue of having a first sentence which is not (particularly) descriptive of the article as a whole.

So, does it work? Well I’ve used this blog as a test, here’s the resultant tweet:

I’ve made use of the TME’s concept and entity extraction features to create hash-tags. #tweet #nsteinswcm http://tinyurl.com/d3ozzn

Personally, I count that as a success.