The first session was titled Open Government data, data mining and the semantic web. This is an area I have a degree of familiarity with but it was interesting to hear stories of wrestling with data on a day to day basis. As well as the general lack of journalism being done with the data published to date.

Alex Wood gave an interesting account of a BBC World Service data journalism project looking at the global occurrence of road accidents. Working initially with World Health Organisation data Alex made the point that the initial dataset helps you ask the right questions but does not necessarily give you the final answer. Scatter plots quickly show anomalies in data and raise issues about how data is collected and categorised. This is when you need to start talking to the people who understand and collect the data.

Chris Taggart spoke of similar challenges when dealing with local data. As the founder of openlylocal.com he has dedicated a number of years to the task of collecting data about  and from local councils and politicians. The messiness of data, varying formats and the lack of id’s to stitch datasets together means openlylocal would not exist if it had not been for passionate individuals dedicating time and resources to it. Chris’s most recent collaboration Open Corporates represents a similar labour of love and co-founder Rob McKinnon spoke of the challenge of stitching together datasets when governments and councils have no common notion of a corporation running through their data. Nigel Shaldbolt was quick to point out these common addresses (uris) to tie together disparate datasets is an important outcome of the data.gov.uk work and its embracing of a approach that works with the web.

Aside from the challenges of collecting the data and shaping it into something meaningful Alex emphasised the importance of telling stories with the data. Kevin Marsh (College of Journalism) made an interesting point that this is not always the case. Newspapers for years have provided data alongside stories; weather information, tv listings and stock prices. Much of the value of a newspaper is shared between stories and pure information and in a digital environment this is no different. In fact the collection of a dataset like openly local can facilitate services that provide useful information at a very local and targeted level. This, it was suggested, is the modern equivalent of the local newspaper’s role as information/data provider. Very local data has potentially a huge, and currently unrecognised, value to audiences.

It was clear from the discussion that working with data is an involved and time intensive process. Perhaps for this reason we have not seen more stories or applications come out of the initial successes of opening data in the UK. Chris did question why organisations like the BBC were not biting his hand off to get access to the open corporates dataset.

The second session was Expert sources in science and health. There was some interesting discussion regarding the use of expert sources in media and the role organisations like the Science Media Centre play in ensuring expert scientists are available. Ben Goldacre raised the issue of transparency in journalism and how few stories link through to original sources like research papers. He cited his long running battle with the BBC to link to sources from their science stories.

Mark Henderson of The Times spoke about the difficulties for journalist in linking to sources. Often at the time of writing a story a research paper will not be published online or be hard to find. Even if you do link to the source publishers are notorious for changing the url’s of papers. Some of these issues have been resolved with the introduction of document object identifiers (DOI) but these are not used consistently across the publishing community.

The relative importance of communication and investigation to journalism were questioned. The panellist emphasised the importance of communication for the mainstream press relaying the developments of science and the playing out of the scientific process. This is in contrast to pointing to experts as a source of facts. Investigative journalism in science is particularly difficult as it requires a deep technical expertise in a given area. Because of this it was suggested the professional blogger community is better placed to provide this analysis work as they often focus solely on their particular area of expertise, have freedom to explore topics and the range of necessary contacts to draw upon.

It did occur to me that the challenges of investigative journalism in science are comparable to the challenges to journalism being done with the datasets currently being opened up. It will take a community of passionate experts to interrogate, analyse and uncover stories in very complex and specialised datasets. In order to be sustainable and to encourage the best data journalism, like the blogging community, it will need the support of the mainstream media.

At the same time there is a role for technologies like Linked Data to reduce the cost of collecting and analysing data and so make data sourced journalism easier to do. A common theme across the sessions was the need for clear and persistent urls for both documents, to aid linking to sources, as well as urls for common things (like corporations) to enable the joining up of the data where the interesting stories lie. As Ben Goldacre said the information architecture of journalism needs to be vastly improved.

In particular I found the following quote from Dan Conver compelling:

“[The] raw material of this information economy is essentially like oil shale: the latent value is obvious, but the cost of extracting these information resources from today’s existing deposits (think web archives) is so high given today’s technology that no one is going to spend a dime to start the project.”

Stijn comments further on this point::
“…Both approaches [emphasis on structured news formats, and rock solid metadata at the story level] wish to extract more value from journalism through structure and relationships. Both approaches have you trade a little hurt during content creation for yet-to-materialize advantages. That’s unavoidable — no such thing as a free lunch.”

Essentially the annotation of news articles with controlled vocabularies. I see the potentail impact of the semantic web slightly differently though do not disagree in principle that the annotation of journalist output is a useful activity. I think perhaps too much emphasis is placed on the extraction of knowledge from editorial assets. I believe the oil shale of journalism is the by product of the process itself.

Guardian is a case in point.  I get the impression that the datablog started out with a hunch it might be of interest to publish some of the spreadsheets that Guardian journalists collected and curated in the process of writing stories. What has been particularly remarkable is that the success of the datablog has probably been greater than the Open Platform. Why? Because it gave access to something that had not been available before.

In my day to day work at the BBC I see numerous resources used by the journalists of a similar value. Planning calenders with lists of news events, research databases and pronunciation resources.

These to me are the true oil shale of journalism.

How the semantic web and linked data play a part in my opinion is no more than reducing the cost and ease of using these data sets in a useful way. I have written before about how we have used semantic web technologies at the BBC to build websites. The combining of BBC editorial assets, with commercial data and open data sources enabled the BBC to do things they would never have dreamed of doing with internally managed data sets or bespoke taxonomies.

By using linked data techniques and simple tools like Google Refine (with the Deri RDF extension) it would be relatively simple to map the datablog spreadsheets to common RDF vocabularies and identifiers. These data sets could then be used to add context, navigation and weave new narrative threads through the Guardian’s editorial output or anyone else’s for that matter. In much the same way Wildlife Finder has used open data sets like Dbpedia to support the delivery of BBC wildlife programmes.

The main issue for the semantic web/linked data is cost incurred due to the current lack of expertise, the barrier to learning new things (in places complicated and unintuitive things), and the relative immaturity of the technologies. With time this will change and savings made from the ease of integrating disparate data sets and the value of mining the ‘raw material of this information economy’ will justify the costs.

Transcript

How does the emergence of the semantic web and its associated technologies change the way we approach user experience design and more specifically information architecture?

In Tim Berners Lee’s original proposal for the web he gave us the basic ingredients to build the web of documents as we experience it today. This gave us a easy means to publish documents, refer to them with url’s and point from one document to another with a hyperlink.

In many ways the web became a victim of its own success. The simplicity with which we could publish documents meant we were soon overwhelmed. At this point Information Architects were employed to group together documents into managable piles.

The process would often take the form of a content audit. Grouping an organisations documents into similar types then giving these groups a label and arranging these groups into small hierarchies. If you were lucky you might also do some user testing. For example a card sort to see if a user of your site would expect to find a document in the place you have grouped it.

The problem with this approach is that if we start out focusing on documents our sites turn out document centric. For example navigation that includes things like pictures, news and features or opinion and archive.

If we step back and think about it. The user coming to our site does not have a mental image of a document but rather the player or team they are interested in. People are interested in things not documents.

This leads us to move away from a document orientated approach to web development to a thing focused one and with this move comes the need for new tools and approaches to information architecture.

One approach at the BBC has been to use Domain Driven Design. DDD encourages you before you have written a line of code or draw a wireframe to collectively understand the things and relationships between them in the problem space you are trying to solve. This model becomes the ubiquitous language used by all members of the project. At this point we can also test our model against the mental model of the user. Ensuring the users mental models are built in the very core of the site.

If we look at an example – when the BBC wanted to open up their archive of wildlife clips  instead of beginning to publish pages for the clips they first published a page for the things of interest and links between these things. So publishing a page for every species, link them to habitats and behaviours.

Each of these pages then links back the species that it relates to. So you soon start to build up a dense network of links between these things. The emphasis in this approach is to shift focus from the content to model. The assets are associated with the things in the model but the model provides the context.

Anyone who has been involved in building even a modest taxonomy for a site will understand the maintenance overhead that this introduces.  In using the rich relationships that an ontology like approach introduces it would be not be feasible to for the BBC to build and manage this product.

Instead the model was populated by sourcing data from the web and stitching it together with common web identifiers. In this case DBpedia . So different sources of data can provide the concepts and – links between concepts – at no extra cost to the BBC. In the cases where a concept is missing, for example in Wikipedia then the team of editorial experts at the BBC will edit or create the concept in Wikipedia. This means not only are we reusing what is already available on the web but in the places where it is wrong or missing we correct it at source so others benefit.

One of the outcomes of focusing on publishing urls for things and creating a dense network of links between them is it has had great benefits in terms of Google rank. In the case of Wildlife Finder some species are being placed above their equivalent page in Wikipedia on UK Google searches.

This approach has not been restricted to wildlife but has been used across the BBC including the World Cup.

The BBC football site at it exists today consisted of a limited number of editorially managed indexes. This means that editorial resource dictated the types of aggregations the site had. So we have no index for the England team or brazil but rather an general and slightly meaningless index called internationals. In addition to this the BBC purchases sports stats from an external provider but at the moment these are not brought together to tell a coherent story.

So in order to solve these challenges the starting point was to think about the things of importance to the world cup as opposed to the documents.

The approach was to focus on the model and then associate content with the things in the model. As the model is device agnostic the views that provide the user experience on top of this can be tailored to be the best we have to offer for a given device.

The starting point of the modelling was to recognise the importance of the event to sport. If we can handle events we can represent the majority of sports. Building upon the existing events ontology we then set about specialising it in order that we could represent the complex structure of a sports competition.

For instance the world cup is a multistage event made up of a group stage and a knockout stage each of which contain rounds and those rounds contain matches.

Once we had developed a model we then decided upon the views that we would want to show the user for a variety of devices. For example html web views would include amongst other things teams, players and groups.

Once we knew the views we wanted to create we could then be sure that if journalists annotated with a select number of tag class types that the model could handle the rest. So we asked them to tag with player, team, competition and venue. By keeping the tagging simple we ensured it would be of high quality.

Here we have a team page for Italy – you can see how the approach starts to bring  stories and data together to tell stories in a more coherent way. Though aggregating assets with tags is not particularly novel when we look at the group pages we can see how this approach is different.

You might remember that we did not ask journalists to tag with group but we are still able to construct this view for users because the model knows which teams played in which group and which players played for which team. No additional editorial intervention was needed to generate these additional views.

By focusing on the model it allowed us to easily integrate a variety of data sources and pull them together to provide a coherent user experience. In addition by tagging content with concept from the model we are increasing the benefits we get from the cost of tagging content. So a tag that has a web scale identifier enables the content to be contextualised in previously impossible ways.

In summary we have looked at a number of ways that semantic web like thinking changes the way we work. Firstly we start developing sites with processes that encourage us to focus us on things and the relationships between them as opposed to the documents, secondly it introduces a culture of building with open vocabularies to add context and links that would never be possible otherwise and it also enables us to maximise the value we get out of our tagging of content. Like the world cup group page example.

All this thinking is heavily influenced by the work of my colleagues at the BBC. Notably Michael Smethurst, Tom Scott, Chris Sizemore and Michael Atherton. For further reading regarding this approach and the work of the BBC there is no better starting point than Michael’s posts on the BBC internet blog and Tom Scott’s personal blog.

In my work with BBC Journalism we have been attempting to take the philosophy of Tom Scott’s Wildlife Finder and applying it to News and Sport. Our starting point has been the Winter Olympics.

The step change was in creating a populated domain model for the games. The things that made up this vocabulary were used by journalists to tag their stories. The tagged stories were then aggregated automatically onto sports indexes. This allowed us to create many more indexes than would have been possible with manual management.

Overall the project was a great success but it raised some interesting questions. The design of the indexes was created by the user experience team. The algorithms were written by developers and informed the ordering of the stories.  This left journalists to simply tag stories and watch their stories appear on indexes they had no control over. It certainly felt like their influence on part of the product had moved a step away from them. This was reflected in journalists’ feedback and the frequent questions about how to game the system to control the order of stories on indexes.

So the questions are:

• How do you enable the journalists to feel in control of the story telling?
• How to do this without introducing tags for value judgements?
• How do you ensure that the site has voice and feels editorialised – as opposed to being simply lists of dynamically aggregated data?

Tom Scott has convinced me the answer is the concept of the collection (and variations on this theme). The collection replicates the manually managed index of stories with a structured list of things. The Wildlife finder example is David Attenborough’s favourite moments. A very simple example for sport might be the best goals of the World Cup. Although this does not seem particularly radical, the beauty of it is that the curatorial layer is built on top of a domain modelled approach.

Because the things that live in our model are associated with assets and data,  the journalist, in selecting a thing to include in a collection pulls data through the system.

Take the same example of the best goals of the World Cup. A journalist would select their top ten goals of the tournament. As the journalist identifies and pulls things through the system into the collection the context around those goals are pulled with them. So the game they were scored in, the importance it had and information about the goal scorers record in the tournament.

Why it is not tagging:

It is important to distinguish the process of creating a collection from the act of tagging. Tagging associates content with things in the domain model. Journalists tagging stories ensure we build up a consistent mapping of the editorial content to the things (and/or concepts) in our domain.

The process of creating collections is closely tied to the editorial judgement of those curating them. Tagging clips with the tag good goal and then anonymously aggregating them is not.

Why it empowers journalists:

The Guardian has found the balance in their topic pages by allowing an editor to pick a story to be displayed at the top of every automated page. But does this go far enough? This still sits very much within the document model of storytelling. What a collection (or similar) begins to allow is a true web adaptation of a news story.

It is the curatorial layer and the use of collections that will allow organisations to reflect voice, perspective and expertise.  How this will improve the experience for the news reader will be the subject of this blog over the forthcoming months.

Could the means by which news organisations adapt their story telling using tools like collections be the key to their ongoing survival?

Collections part 1: Collections of links

A topic that interests me is the relationship between Linked Data and controlled vocabularies, to steal a phrase from Tom Coates (native to the web), and Linked Data’s call for vocabularies native to the web.

Let’s look at it this way – if you were asked to creating a web presence for an individual or organisation today you might propose the following:

1. Make interesting documents public.
2. Publish using web standards such as HTML.
3. Provide useful information about the individual or organisation.
4. Link to similar documents where you can.
5. Then if the documents are useful and you are gracious in linking to others they will link back to you.

It is apparent that Linked Data asks the same of controlled vocabularies.

1. Make your vocabularies public.
2. Publish using the web standards of Linked Data.
3. For each concept provide useful information for humans and machines.
4. Link to other vocabularies (map concepts) where you can.
5. If you have provided a useful set of concepts and relationships others will link back to you, increasing the value of your CV.

It could seem crazy at the moment to give away your taxonomy for free but it would have been a similarly difficult argument to convincing an organisation to have a web presence ten or fifteen years ago.

Linked Data is already showing the benefits of this approach. When we open-source vocabularies we can be much more ambitious in the richness of relationships and complexity of structures.  In my talk I mentioned that the, wonderful, Wildlife Finder would not have been feasible had the ontologies not been publically available to use and build upon.  A Wildlife Finder built on a far simpler BBC bespoke taxonomy of animals, habitats and behaviours would have been a far poorer and more costly proposition. Martin expands on this in his Guardian post.

Recently we have seen the likes of LCSH and New York Times vocabularies joining the Linked Data cloud and becoming web native vocabularies. I suspect the success and survival of many vocabularies will depend on how quickly their owners can grasp the importance of becoming open and native to the web.

Government data panel - photo by Zac Beauvais

Having just recovered from last week’s London Linked data meet up.  I thought it was time to collect together the talks and commentary from the day.

Georgi and I are particularly grateful to everyone for coming, in particular those that spoke.  A special thank you also to  Talis for picking up the bar tab.

I think Zach Beauvais summarised the day nicely in his post:

‘The day was a storming success, with talks and presentations from all over the Linked Data community: from academia to startups. I think the organisers were slightly overwhelmed, because in the end there were nearly 200 people there, making use of the Talis-sponsored bar well into the evening. Apart from being a good opportunity to catch up with people, this meetup had the feeling of a guild-meet of Linked Data professionals—with lots of different perspectives over similar problems.’

Presentations

Here are links to the presentations so far and I will add the rest as they become avaliable:

Tom Scott / Yves Raimond (BBC)

“Contextualising BBC programmes using the Data Web”

The BBC, following the Linked Data principles, now publishes a URI for every TV and Radio programme it broadcasts this allows people to browser by schedule, genre, format and a-z.

More recently we have published URIs for music artists, animal species and habitats – these pages not only provide useful information in their own right but also allow us to re-contextualise the programme information helping users to discover new content and new patterns.

Leigh Dodds (Talis)

“DataIncubator.org — What Is It & What’s In It?“

This talk will introduce the dataincubator.org project which, supported by the Talis Connected Commons scheme, provides an umbrella project for publishing public domain linked data, with the aim of demonstrating to the original publishers the benefits of Linked Data, as well as a means to build on the community’s efforts. The talk will review the project and some of the datasets that have currently been made available.

Andrew Walkingshaw (Timetric)

“Time to build: storing, sharing and analysing statistics with Timetric, a Web-native service for managing numbers”

Timetric is a Web service which lets users upload, download, visualize and set up calculations on over a hundred thousand different measurements, the values of all of which are tracked over time. But how would you build that, and when you have, who’d want it? In this talk, we’ll discuss the lessons we’ve learned in building a service for sharing open data on the Web and in building a business around that service.

Michael Smethurst, Matthew Wood (BBC)

“Rights, Privacy and Linked Data“

Georgi Kobilarov (Freie Universität Berlin / DBpedia)

“Integrating Linked Data”

Nigel Shadbolt (University of Southampton)

“Hard Research Challenges in the Web Of Linked Data: The EPSRC EnAKTinG Project”

The UK Engineering and Physical Sciences Research Council (EPSRC) has funded a three year two million pound project at the University of Southampton to investigate the challenges represented by the Web of Linked Data. Nigel Shadbolt and Tim Berners-Lee are two of the Principal Investigators on this project. In this brief presentation the projects aims and ambition will be outlined – together with progress to date.

Libby Miller (BBC / NoTube)

“Beancounter – telling you about you”

Increasing automation means that lots of data is available about what you do, including what you watch and listen to. This means that companies or researchers can mine information about your activities and use them to make predictions about what you might like, and what they might be able to sell you. Beancounter uses attention data from multiple sources, enhanced by linked data, to tell you what you are *really* interested in – rather than what you *think* you are interested in. It puts the control about what sources can be mined in your hands, and limits what companies can do with the outputs. Beancounter is a product of the NoTube EU project.’

Richard Cyganiak (DERI Galway)

“Sig.ma – Live Views on the Web of Data”

Increasing amounts of high-quality data are being published on the web of data, but a lack of applications for searching and browsing it makes access and exploration difficult. Sig.ma is a new user interface that improves upon previous ones by offering fine-grained control over source selection, fuzzy entity matching, and schema and value consolidation. Sig.ma is online at http://sig.ma/… and provides the fastest way yet to get an overview about the data available on a given topic.

Jun Zhao (University Oxford)

“Linked Data for Connecting Medicine Knowledge”

Mischa Tuffield / Steve Harris (Garlik)

“Making FOAF useful: http://foaf.qdos.com/ “

Since the beginning of the Linked Data Movement, a fair chunk of the resolvable RDF found on the web has been FOAF data. This talk will involve a brief overview of what FOAF represents, a list of the services we provide, how we go about saving public and private FOAF data, whilst presenting insight into the technologies used to underpin the services on foaf.qdos.com.

Ian Millard (University of Southampton)

“RKB-Explorer”

The RKBExplorer.com application provides a simple interface over multiple Linked Data sources to assist with the discovery and exploration of related activities with the academic research domain.

This talk will briefly summarise issues and experiences regarding interoperation of multiple sources, and outline some of the services we offer that can be used by all.

Panel: Government Data

Chair: Carol Tullo (Office of Public Sector Information)
Tim Bernes-Lee
Paul Miller (Cloud of Data)
Nigel Shadbolt (University of Southampton)
Mark Birbeck (webBackplane)
John Goodwin (Ordnance Survey)

A good summary of this panel in a blog post by Jane Stevenson and in Zac’s post.

‘It gave a good sense of what is happening at the moment with Linked Data and what the issues are. Tim Berners-Lee (inventor of the Web) and Nigel Shadbolt talked about the decision to prioritise UK government data within the Linked Data project – clearly it is of great value for a whole host of reasons, and a critical mass of data can be achieved if the government are on board, and also we should not forget that it is ‘our data’ so it should be opened up to us – public sector data touches all of us, businesses, institutions, individuals, groups, processes, etc.’

Thank you to Carol Tullo for doing such a good job of chairing the session.

Panel: Future of Journalism

Chair: Paul Bradshaw (Online Journalism)
Martin Belham (The Guardian)
John O’ Donovan (BBC)
Dan Brickley
Leigh Dodds (Talis)

A number of posts are avaliable from this session from the wonderful chair Paul Bradshaw and panelist Martin Belham of the Guardian.

Data and the future of journalism panal discussion: Paul Bradshaw

Linked data future of journalsim part 1: Martin Belham

Linked data future of journalsim part 2: Martin Belham

Tom Heath’s “Linked Data – The Story So Far” was a fantastic way to finish the evening and really captured the challenges that lie ahead.

Update: Two further post from Martin Belham: Part 1 and Part 2.

Some of the largest and most successful websites have taken simple narrative structures and made them scale successfully.  In the mold of the fairytale “once upon a time” and “they all lived happily ever after” these sites have come to own their simple narrative structures and this has played a significant part in their success.  Some familiar examples:

• Customers Who Bought This Item Also Bought – noun (book) verb (also bought) noun (book)
• Buy it now – noun (user) verb (buy) noun (item)
• Such and such wrote on your Wall – noun (friend) verb (wrote on) noun (wall)

These simple noun-verb-noun narratives should be familiar and are very much part of the brand of these sites. This is a result of them getting these narratives to scale and ensuring there is the quality of data to back them up.

Now in order to make sure these narratives are applied consistently as the site accumilates content these structures need to be understood by your application. This means the noun-verb-noun structures must be encoded into your domain model ( and so your database) from the outset. Designing the site in this way means that as new content, pages and data are added to the site these narrative structures will be automatically created. This guarantees new pages are incorporated into the site and automatically become a scene in the sites larger story.

Weak and strong narrative structures

As we move from flat published pages to large dynamically created sites we need to think more and more about the primary narrative structures. These user journeys will be encoded into the very core of the site and you will want to be confident you have selected the right ones and that there is the data to back them up.

One of the strengths of the BBC News site is its contextual navigation with strong narrative. For example a BBC News story about Kosovo will carry an explicit user journey to the background story of the independence of Kosovo. This is in contrast to tags. Tags help to open up new user journeys but are weak in narrative, taking the form ‘this content is about this tag’. Related links also often fall into this category of weak narrative. One of the problems with rich narrative structure is that they are difficult to scale, this poses a significant challenge.

Web-scale narratives

When George Lucas was looking for a narrative structure for the beginning of his Star Wars films he used a well understood simple narrative structure, ‘once upon a time’.

He knew that this would be something that his audience would immediately understand.

The dream of the Semantic Web project follows a similar logic. Take the simple narrative structures that have been so successful in creating user journeys within large scalable websites and apply them to the web at large.  This means narratives (in the form of domain models and ontologies) that are not limited to a single site. Not just ‘people who bought this on Amazon also bought this’ rather ‘people who bought this on the web also bought this’ web-scale narrative structures. This will not only help create more coherent user journeys across the web but also provides more structure to help machine understanding.

“Unless anyone here works for Philips, I’m fairly certain that nobody in this room wants or will ever buy a networked fridge.”

http://www.currybet.net/cbet_blog/2008/09/euroia2008_part1.php

Fair point but I wanted to revisit the concept with regard to the big challenge of this century; climate change and energy conservation. Thomas Friedman’s book Hot, Flat and Crowded is a nice summary of some of the issues and possible solutions.

The problem:
If we continue on our current path CO2 levels will double (to 560ppm) around the mid-century and will triple by 2075. A situation we have not been in for 650,000 years. We don’t know what it will be like to live in a 560ppm CO2 world let alone an 800ppm one.

“So now we have a target: We want to avoid the doubling of CO2 by mid-century, to do it we need to avoid emission of 200 billion tons of carbon as we grow between now and then.”

Thomas Friedman

Solutions:
Freidman identifies a number of targets that need to be met. One of them is to cut electricity use in homes, offices, and stores by 25%. A way that this might be achieved according to Friedman is to become more intelligent about energy use and the development of an Energy Internet. Energy distribution and consumption is currently stuck in the 50’s and has failed to embrace the IT revolution.

The concept of an Energy Internet was originally conceived in an Economist article:

“Energy visionaries imagine a “self-healing” grid with real-time sensors and “plug and play” software that can allow scattered generators or energy-storage devices to attach to it. In other words, an energy internet.”

http://www.economist.com/science/tq/displaystory.cfm?story_id=E1_NQSGJRR

Amongst other things this would mean more intelligent appliances in the home that can negotiate their energy needs with the grid as well as communicating to the homeowner the worst offenders in growing energy bills. Friedman imagines what it might be like to live with a smart grid.

“..an Energy Internet in which every device – from light switches to air conditioners, to basement boilers, to car batteries and power lines and power stations – incoporate microchips that could inform your utility of the energy level at which it was operating, take instructions from you or your utility as to when it should operate and at what level of power, and tell your utility when it wanted to purchase or sell electricity. You and your utility now have two-way communications.”

Thomas Freidman

So the smart fridge is not dead but it just won’t be doing the weekly shop for us, it will be helping save the planet (or at least your energy bill).

Clearly ubiquitous computing is closely tied to the Semantic Web. Until machines can parse the web on our behalf we are stuck with large screens so that we can parse the data for them. The smart fridge will need Semantic Web technologies and so link into a larger body of data about our energy use; where it comes from (clean or dirty), how it is being used in the home and the damage it is doing. Targeted advertising will illustrate how new, more efficient, appliances will impact our energy use and so on into the graph…

Currently we have little context to understand our energy use and context is king when it comes to education and driving real changes in behaviour. Perhaps the Energy Internet and the tackling of one of the big problems will be the making of the Semantic Web project.

Update: The Talis people have written a nice post about semweb and the home: http://blogs.talis.com/nodalities/2008/12/smart-stuff.php

Chris covers most of the issues we discussed in his post. But one thing we did not cover is the interesting way Wikipedia handles categories. I have tried to find discussion about this approach with no success. It will be an interesting issue to raise in the ISKO mail group.

As opposed to making one entry a parent of another, as we might do in a taxonomy. In Wikipedia categories (groupings of concepts) are treated as a completely different type of entry. This means we can have groups like Dog-related_professions_and_professionals without falling into the trap of treating them like concepts (entries) in their own right.

The two activities of defining a concept and grouping associated concepts are separated. The result of this is Wikipedia entries remain topics of interest that are discrete and clearly defined. This make them ideal for sanity checking that our choice of topic aggregation page are discrete and clearly defined as discussed in the previous ‘what makes a good topic aggregation page?’ Post.

Update: Bob Bater has written a nice post on  this topic on the ISKO UK blog.

Conclusion? The Wikipedia categorization system reflects but does not consistently apply the principles of KO as expounded in the formal literature. It is nevertheless interesting because it might well represent what results when folksonomy meets formal KO and agrees to a compromise.

http://iskouk.wordpress.com/2008/09/22/wikipedias-approach-to-categorization/