Tag Archive for 'wiki'

Explaining APML: what it is & why you want it

Published
by
Elias Bizannes
on October 10, 2007
in DataPortability
. 17 Comments

Lately there has been a lot of chatter about APML. As a member of the workgroup advocating this standard, I thought I might help answer some of the questions on people’s minds. Primarily - “what is an APML file”, and “why do I want one”. I suggest you read the excellent article by Marjolein Hoekstra on attention profiling that she recently wrote, if you haven’t already done so, as an introduction to attention profiling. This article will focus on explaining what the technical side of an APML file is and what can be done with it. Hopefully by understanding what APML actually is, you’ll understand how it can benefit you as a user.

APML - the specification
APML stands for Attention Profile Markup Language. It’s an attention economy concept, based on the XML technical standard. I am going to assume you don’t know what attention means, nor what XML is, so here is a quick explanation to get you on board.

Attention
There is this concept floating around on the web about the attention economy. It means as a consumer, you consume web services - e-mail, rss readers, social networking sites - and you generate value through your attention. For example, if I am on a Myspace band page for Sneaky Sound System, I am giving attention to that band. Newscorp (the company that owns MySpace) is capturing that implicit data about me (ie, it knows I like Electro/Pop/House music). By giving my attention, Newscorp has collected information about me. Implicit data are things you give away about yourself without saying it, like how people can determine what type of person you are purely off the clothes you wear. It’s like explicit data - information you give up about yourself (like your gender when you signed up to MySpace).

Attention camera

I know what you did last Summer

XML
XML is one of the core standards on the web. The web pages you access, are probably using a form of XML to provide the content to you (xHTML). If you use an RSS reader, it pulls a version of XML to deliver that content to you. I am not going to get into a discussion about XML because there are plenty of other places that can do that. However I just want to make sure you understand, that XML is a very flexible way of structuring data. Think of it like a street directory. It’s useless if you have a map with no street names if you are trying to find a house. But by having a map with the street names, it suddenly becomes a lot more useful because you can make sense of the houses (the content). It’s a way of describing a piece of content.

APML - the specification
So all APML is, is a way of converting your attention into a structured format. The way APML does this, is that it stores your implicit and explicit data - and scores it. Lost? Keep reading.

Continuing with my example about Sneaky Sound System. If MySpace supported APML, they would identify that I like pop music. But just because someone gives attention to something, that doesn’t mean they really like it; the thing about implicit data is that companies are guessing because you haven’t actually said it. So MySpace might say I like pop music but with a score of 0.2 or 20% positive - meaning they’re not too confident. Now lets say directly after that, I go onto the Britney Spears music space. Okay, there’s no doubting now: I definitely do like pop music. So my score against “pop” is now 0.5 (50%). And if I visited the Christina Aguilera page: forget about it - my APML rank just blew to 1.0! (Note that the scoring system is a percentage, with a range from -1.0 to +1.0 or -100% to +100%).

APML ranks things,  but the concepts are not just things: it will also rank authors. In the case of Marjolein Hoekstra, who wrote that post I mention in my intro, because I read other things from her it means I have a high regard for her writing. Therefore, my APML file gives her a high score. On the other hand, I have an allergic reaction whenever I read something from Valleywag because they have cooties. So Marjolein’s rank would be 1.0 but Valleywag’s -1.0.

Aside from the ranking of concepts (which is the core of what APML is), there are other things in an APML file that might confuse you when reviewing the spec. “From” means ‘from the place you gave your attention’. So with the Sneaky Sound System concept, it would be ‘from: MySpace’. It’s simply describing the name of the application that added the implicit node. Another thing you may notice in an APML file is that you can create “profiles”. For example, the concepts about me in my “work” profile is not something I want to mix with my “personal” profile. This allows you to segment the ranked concepts in your APML into different groups, allowing applications access to only a particilar profile.

Another thing to take note of is ‘implicit’ and ‘explicit’ which I touched on above - implicit being things you give attention to (ie, the clothes you wear - people guess because of what you wear, you are a certain personality type); explicit being things you gave away (the words you said - when you say “I’m a moron” it’s quite obvious, you are). APML categorises concepts based on whether you explicitly said it, or it was implicitly determined by an application.

Okay, big whoop - why can an APML do for me?
In my eyes, there are five main benefits of APML: filtering, accountability, privacy, shared data, and you being boss.

1) Filtering
If a company supports APML, they are using a smart standard that other companies use to profile you. By ranking concepts and authors for example, they can use your APML file in the future to filter things that might interest you. As I have such a high ranking for Marjolein, when Bloglines implements APML, they will be able to use this information to start prioritising content in my RSS reader. Meaning, of the 1000 items in my bloglines reader, all the blog postings from her will have more emphasis for me to read whilst all the ones about Valleywag will sit at the bottom (with last nights trash).

2) Accountability
If a company is collecting implicit data about me and trying to profile me, I would like to see that infomation thank you very much. It’s a bit like me wearing a pink shirt at a party. You meet me at a party, and think “Pink - the dude must be gay”. Now I am actually as straight as a doornail, and wearing that pink shirt is me trying to be trendy. However what you have done is that by observation, you have profiled me. Now imagine if that was a web application, where this happens all the time. By letting them access your data - your APML file - you can change that. I’ve actually done this with Particls before, which supports APML. It had ranked a concept as high based on things I had read, which was wrong. So what I did, was changed the score to -1.0 for one of them, because that way, Particls would never show me content on things it thought I would like.

3) Privacy
I joined the APML workgroup for this reason: it was to me a smart away to deal with the growing privacy issue on the web. It fits my requirements about being privacy compliant:

  • who can see information about you
  • when can people see information about you:
  • what information they can see about you

The way APML does that is by allowing me to create ‘profiles’ within my APML file; allowing me to export my APML file from a company; and by allowing me to access my APML file so I can see what profile I have.

drivers

Here is my APML, now let me in. Biatch.

4) Shared data
An APML file can, with your permission, share information between your web-services. My concepts ranking books on Amazon.com, can sit alongside my RSS feed rankings. What’s powerful about that, is the unintended consequences of sharing that data. For example, if Amazon ranked what my favourite genres were about books - this could be useful information to help me filter my RSS feeds about blog topics. The data generated in Amazon’s ecosystem, can benefit me and enjoy a product in another ecosystem, in a mutually beneficial way.

5) You’re the boss!
By being able to generate APML for the things you give attention to, you are recognising the value your attention has - something companies already place a lot of value on. Your browsing habits can reveal useful information about your personality, and the ability to control your profile is a very powerful concept. It’s like controlling the image people have of you: you don’t want the wrong things being said about you. :-)

Want to know more?
Check the APML FAQ. Othersise, post a comment if you still have no idea what APML is. Myself or one of the other APML workgroup members would be more than happy to answer your queries.

Understand your content

Published
by
Elias Bizannes
on September 24, 2007
in Attention economy
. 0 Comments

I picked up a book my parents used on their recent trip to Greece, which was a guidebook of the Peloponnese. Flicking through this paper book reminded me of my thoughts of how the content business is so rife with piracy. Especially with an online world now, people can copy content - images, text, audio - and mash it up into their own creation. It seems crazy but why do people enter a business like that?

The Information Sector is not only a big money maker, but very unique as well. Yes, it can be copied and ripped off - unlike a barbie doll where its form can’t really be manipulated into a new product. However different from selling barbies, is that information products do things that are very unique in this world and extremely powerful. In my view there are four types of information product, which can be explained under the categories of data or culture.

Data

New data
A friend and aspiring politician, once said to me that “information is the currency of politics”. Reuters, the famed news organisation that supplies breaking news to media outfits across the world - derives 90% of its revenue from selling up-to-the-minute financial information to stockbrokers and the like who profit on getting information before others. New information, like what the weather will be tomorrow, loses value with time (no many care what the weather was eight days ago). But people are willing to pay a price, and a big one, to get access to this breaking news because it can help make decisions.

Old data
On the flip side, old information can be very valuable because of the ability to conduct research and analysis. Search engines effectively fit into this segment of the information economy, because they can query past news and knowledge to produce answers. Extending the weather example, being about to find out that data eight days ago along with the weather exactly one, five and ten years ago - can help you identify trends that, for example, validates the global warming theory.

Culture

Analysis
The third category of information products, I call them simply analysis because what they are is unique insight into things. We all have access to the same news for example, but it takes a smart thinker to create a prediction, by pulling the pieces together and creating new value from them. Analytical content usually gets plagiarised by students writing essays, but its also the stuff that shapes peoples perceptions in world-changing ways.

Entertainment
One of the most powerful uses of content is the way it can impact people - entertainment type content is the stuff that generates emotion in people. Emotions are a key human trait that you should keep in mind in any decision - no matter how logical someone is, the emotional self can overtake. A documentary that portrays an issue negatively, and that can generate an angry response in a person, is the stuff that can topple governments and corporations.

Not all information is equal
If you are a content creator, you need to accept that other people can copy your creation. The key is to understand what type of content you are creating, and develop a content strategy that exploits its unique characteristics.

Information products need different strategies in order to effectively monetise them. Below is a brief discussion which extends on the above to help you understand.
New data
With this type of content, the value is in the time; the quicker that information can be accessed, the more useful it is. News items (like current affairs) fit into this category. As a news consumer, I don’t care how I get my news, but I care about how quickly I can get it. It’s for this reason I no longer read newspapers, yet through various technologies like RSS and my mobile phone, that I probably consume more news than ever before.

You should sell this data based on access - the more you pay, the quicker the access. Likewise, the ability to enable multiple outputs is key - you need to be able to deliver your content to as many different places as possible: SMS, email, RSS etc. You should not discriminate on the output; the value is on the time.

If you create news breaks, why are you wasting your time on who can access that information, because of the threat that someone can copy it? If the value is in the time, who cares who copies it because by the time they republish it, its already lost value. A flash driven site like the Australian Financial Review is an example of a management that doesn’t realise this.

Old data
A recent example of action in this space is the New York Times who have recently removed their paid subscription wall, which was previously only available via subscription but now can be accessed by anyone for free. This is a smart business move, because if you are selling archived content, you will make more money by having more people know what exists. A paid wall limits people using it which decreases the opportunity for consumption: you a relying on a brand only to create demand. If you are website with a lot of historical content - restricting access is stupid because you are effectively asking people to pay for access to something that they have no idea what value it holds for them. It’s a bit like traveling - if you’ve never been overseas, you don’t know what you are missing out on. Give people a taste of the travel bug, and they will never be able to sit still.

Unlike new data where the value is based on time, old data finds value on accessibility. People will place value on things like search, and the ability to find relevant content through the mountains of content available. Here the multitude of outputs doesn’t matter, because researchers have all the time in the world. What matters is a good interface, and powerful tools to mine the data: the value is on being to find information. You shouldn’t charge people on access to the content; where you will make money is on the tools to mine the data.

Analysis
This type of content is difficult to create, but easily ripped off by other people - just think of how rife plagiarism is with schools and universities, where the latter treats plagiarism as a crime just short of murder. You can distinguish this type of content as it demonstrates the ability to offer content that is was produced from a common set on inputs that anyone could access, and creating a viewpoint that only a certain type of person could create. The value is on the unique insight.

Despite the higher intlellect to product, it unfortunately is content that is harder to capitalise on. A lot of technology blogs feel the pressure of moving into a more news style than analytical service because news is what gets eyeballs. If you are a blogger looking to make money - the new data approach above should be your strategy. But if you are a blogger trying to build your brand - do analysis. The consequence with analysis is that its harder to do, so you shouldn’t feel pressured to produce more content. I’ve noticed a trend for example, that if I post more blog postings, I will get more traffic. But on the same token, more postings puts more pressure on me, which means less quality content. Understand that the value of analysis isn’t dependent on time. Or better said, the value of analysis is not how quickly it gets pumped out and realised, but how thoroughly it gets incubated as an idea and later communicated.

The value for analysis is clarity and ability to offer new thoughts. To look at the relationship with advertising models, new data like news (discussed above) typically gets higher viewers - which works for the pageview model (the more people refreshing, the more CPMs). Analysis, on the other hand, works with the time spent model. Take advantage of the engagement you have with those types of readers, because you are cultivating a community of smart people - there can be a lot more loyalty with that type of readership.

Entertainment
My sister downloads the Chaser’s War on Everything as a podcast. She first came across them on the radio, but she now downloads the podcasts religiously. Even though I knew about the Chaser’s efforts for years in their various products, I didn’t realise they were still around. If the last few weeks, I have been noticing my friends bring up the shows they are doing. The value in this content was the ability to make people laugh, due to their unique stunts. Their brand is built because of word of mouth recommendations.

Like analysis, entertainment can be a very hard thing to generate because it relies on unique thinking. With a strong brand, people will pay for access to that content. Although it may seem that the viral spreading of funny content for free is a nightmare for a content producer trying to collect royalties, it’s actually a good thing because it entrenches the brand: more people will find out about it. The nature of entertainment, like analysis, is that it is difficult to do repeatedly. Sure people can copy your individual tricks - but they can only do so after the fact. They can’t pre-anticipate the next thing you will do; because unlike breaking news which is on how quickly you can pump out content, entertainment content requires a unique creative process to produce it.

The key with entertainment content, is to build a relationship with an audience and to sustain it. Create a predictable flow of content. Encourage people copying it, because all it does it get more people wanting to see what you come up with next. If it wasn’t for Stephen Colbert’s clips on Youtube, I would never have realised his brilliance. Not knowing he existed, means a DVD set of his shows means nothing to me (but which holds a lot of value now). The value of entertainment is to generate emotions in people repeatedly. Emotions are a powerful influence on human behaviour - master that and you can be dangerous!

Concluding thoughts
This posting only touches on the issues, but what I suggest is that creators of content need to look at what type of content they are producing, for them to exploit its unique aspects. Content represents human ideas, and content isn’t distiguished by a physical form. The theft of your content should be a given and can actually help you. Depending on what that content is, there may be natural safeguards that make it irrelevant (ie, the time value of news).

Don’t get the Semantic Web? You will after this

Published
by
Elias Bizannes
on September 12, 2007
in semantic web
. 4 Comments

Prior to 2006, I had sort of heard of the Semantic Web. To be honest, I didn’t know much – it was just another buzzword. I’ve been hearing about Microformats for years, and cool but useless initiatives like XFN. However to me it was simply just another web thing being thrown around.

Then in August 2006, I came across Adrian Holovaty’s article where he argues journalism needs to move from a story-centric world to a data-centric world. And that’s when it dawned on me: the Semantic web is some serious business.

I have since done a lot of reading, listening, and thinking. I don’t profess to be a Semantic Web expert – but I know more than the average person as I have (painfully) put myself through videos and audios of academic types who confuse the crap out of me. I’ve also read through a myriad of academic papers from the W3C, which are like the times when you read a novel and keep re-reading the same page and still can’t remember what you just read.

Hell – I still don’t get things. But I get the vision, so that’s what I am going to share with you now. Hopefully, my understanding will benefit the clueless and the skeptical alike, because it’s a powerful vision which is entirely possible

1) The current web is great for humans; useless for machines
When you search for ambiguous terms, at best, search engines can algorithmically predict some sort of answer that partially answers your query. Sometimes not. But the complexity of language, is not something engineers can engineer to deal with. After all, without ambiguity of natural languages, the existence of poetry is impossible.

Fine.

What did you think when you read that? As in: “I’ve had it – fine!” which is like another way of saying ok or agreeing with something. Perhaps you thought about that parking ticket I just got – illegal parking gets you fined. Maybe you thought I am applauding myself by saying that was one fine piece of wordcraftship I just wrote, or said in another context, like a fine wine.

Language is ambiguous, and depending on the context with other words, we can determine what the meaning of the word is. Search start-up company Powerset, which is hoping to kill Google and rule the world, is employing exactly this technique to improve search: intelligent processing of words depending on context. So by me putting in “it’s a fine”, it understands the context that it’s a parking ticket, because you wouldn’t say “it’s a” in front of ‘fine’ when you use it to agree with something (the ‘ok’ meaning above).

But let’s use another example: “Hilton Paris” in Google – the worlds most ‘advanced’ search engine. Obviously, as a human reading that sentence, you understand because of the context of those words I would like to find information about the Hilton in Paris. Well maybe.

Let’s see what Google comes up with: Of the ten search results (as of when I wrote this blog posting), one was a news item on the celebrity; six were on the celebrity describing her in some shape or form, and three results were on the actual Hotel. Google, at 30/70 – is a little unsure.

Why is Paris Hilton, that blonde haired thingy of a celebrity, coming up in the search results?

Technologies like Powerset apparently produce a better result because it understands the order of the words and context of the search query. But the problem with these searches, isn’t the interpretation of what the searcher wants – but also the ability to understand the actual search results. Powerset can only interpret so much of the gazilions of words out there. There is the whole problem of the source data, no just the query. Don’t get what I mean? Keep reading. But for now, learn this lesson

Computers have no idea about the data they are reading. In fact, Google pumping out those search results is based on people linking. Google is a machine, and reads 1s and 0s – machine language. It doesn’t get human language

2) The Semantic web is about making what human’s read, machine readable
Tim Berner’s Lee, the guy that invented the World Wide Web and the visionary behind the Semantic Web, prefers to call it the ‘data web’. The current web is a web of documents – by adding this extra data to content – machines will be able to understand it. Metadata, is data about data.

A practical outcome of having a semantic web, is that Google would know that when it pulls up a web page regardless of the context of the words – it will understand what the content is. Think of every word on the web, being linked to a master dictionary.

The benefit of the semantic web is not for humans – at least immediately. The Semantic Web is actually pretty boring with what it does – what is exciting, is what it will enable. Keep reading.

3) The Semantic web is for machines to interpret, not people
A lot of the skeptics of the semantic web, usually don’t see the value of it. Who cares about adding all this extra meta data? I mean heck – Google still was able to get the website I needed – the Hilton in Paris. Sure, the other 60% of the results on that page were irrelevant, but I’m happy.

I once came across a Google employee and he asked “what’s the point of a semantic web; don’t we already enough metadata?” To some extent, he’s right – there are some websites out there that have metadata. But the point of the semantic web is so that machines once they read the information, can start thinking like how a human would and connecting it to other information. There needs to be across the board metadata.

For example, my friend Michael was recently looking to buy a car. A painful process, because there are so many variables. So many different models, different makes, different dealers, different packages. We have websites, with cars for sale neatly categorised into profile pages saying what model it is, what colour it is, and how much. (Which may I add, are hosted on multiple car sites with different types of profiles). A human painfully reads through these profiles, and computes as fast as a human can. But a machine can’t read these profiles.

Instead of wasting his (and my) weekends driving around Sydney to find his car, a machine could find it for him. So, Mike would enter his profile in – what he requires in a car, what his credit limit is, what his prior history with cars are – everything that would affect his judgement of a car. And then, the computer can query every online website with cars to match the criteria. Because the computer can interpret these websites across the board, it can evaluate and it can go back to Michael and say “this is the car for you, at this dealer – click yes to buy”.

The semantic web is about giving computers the information to be able to interpret data, so that it can do what they do really well – compute.

4) A worldwide database
What essentially Berner’s Lee envisions, is turning the entire world wide web into a database that can be queried. Currently, the web looks like Microsoft Word – one swab of text. However, if that swab of text was neatly categorised in an Excel spreadsheet, you could manipulate that data and do what you please – create reports, reorder them, filter, and do whatever until your heart is content.

At university, I was forced to do an Information Systems subject which was essentially about the theory of databases. Damn painful. I learned only two things from that course. The first thing was that my lecturer, tutor, and classmates spoke less intelligible English than a caterpillar. But the second thing was that I learned what information is and how it differs from data. I am now going to share with you that lesson, and save you three months of your life.

You see, data is meaningless. For example, 23 degrees is data. On its own, it’s useless. Another piece of data in Sydney. Again, - useless. I mean, you can think all sorts of things when you think of Sydney, but it doesn’t have any meaning.

Now put together 23 degrees and Sydney, and you have just created information. Information is about creating relationships between data. By creating a relationship, an association, between these two different pieces of data – you can determine it’s going to be a warm day in Sydney. And that is what information is: Relationship building; connecting the dots; linking the islands of data together to generate something meaningful.

The semantic web is about allowing computers to be able to query the sum of human knowledge like one big database to generate information

Concluding thoughts
You are probably now starting to freak out and think “Terminator” images with computers suddenly erupting form under your computer desk, and smashing you against the wall as a battle between humans and computers begins. But I don’t see it like that.

I think about the thousands of hours humans spend trying to compute things. I think of the cancer research, whereby all this experimentation occurring in labs, is trying to connect new pieces of data with old data to create new information. I think about computers being about to query the entire taxation legislation to make sure I don’t pay any tax, because it knows how it all fits together (having studied tax, I can assure you – it takes a lifetime to only understand a portion of tax law). In short, I understand the vision of the Semantic web as a way of linking things together, to enable computers to compute – so that I can sit on my hammock drinking my beer, as I can delegate the duties of my life to the machines.

All the semantic web is trying to do, is making sure everything is structured in a consistent manner, with a consistent dictionary behind the content, so that a machine can draw connections. As Berner’s Lee said on one of the videos I saw: “it’s all about creating links”.

The process to a Semantic Web is boring. But once we have those links, we can then start talking about those hammocks. And that’s when the power of the internet - the global network - will really take off.

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