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Lak11 Week 3 and 4 (and 5): Semantic Web, Tools and Corporate Use of Analytics

Two weeks ago I visited Learning Technologies 2011 in London (blog post forthcoming). This meant I had less time to write down some thoughts on Lak11. I did manage to read most of the reading materials from the syllabus and did some experimenting with the different tools that are out there. Here are my reflections on week 3 and 4 (and a little bit of 5) of the course.

The Semantic Web and Linked Data

This was the main topic of week three of the course. Basically the semantic web has a couple of characteristics. It tries to separate the presentation of the data and the data itself. It does this by structuring the data which then allows linking up all the data. The technical way that this is done is through so-called RDF-triples: a subject, a predicate and an object.

Although he is a better writer than speaker, I still enjoyed this video of Tim Berners-Lee (the inventor of the web) explaining the concept of linked data. His point about the fact that we cannot predict what we are going to make with this technology is well taken: “If we end up only building the things I can imagine, we would have failed“.

The benefits of this are easy to see. In the forums there was a lot of discussion around whether the semantic web is feasible and whether it is actually necessary to put effort into it. People seemed to think that putting in a lot of human effort to make something easier to read for machines is turning the world upside down. I actually don’t think that is strictly true. I don’t believe we need strict ontologies, but I do think we could define more simple machine readable formats and create great interfaces for inputting data into these formats.

Use cases for analytics in corporate learning

Weeks ago Bert De Coutere started creating a set of use cases for analytics in corporate learning. I have been wanting to add some of my own ideas, but wasn’t able to create enough “thinking time” earlier. This week I finally managed to take part in the discussion. Thinking about the problem I noticed that I often found it difficult to make a distinction between learning and improving performance. In the end I decided not to worry about it. I also did not stick to the format: it should be pretty obvious what kind of analytics could deliver these use cases. These are the ideas that I added:

  • Portfolio management through monitoring search terms
    You are responsible for the project management portfolio learning portfolio. In the past you mostly worried about “closing skill gaps” through making sure there were enough courses on the topic. In recent years you have switched to making sure the community is healthy and you have switched from developing “just in case” learning intervention towards “just in time” learning interventions. One thing that really helps you in doing your work is the weekly trending questions/topics/problems list you get in your mailbox. It is an ever-changing list of things that have been discussed and searched for recently in the project management space. It wasn’t until you saw this dashboard that you noticed a sharp increase in demand for information about privacy laws in China. Because of it you were able to create a document with some relevant links that you now show as a recommended result when people search for privacy and China.
  • Social Contextualization of Content
    Whenever you look at any piece of content in your company (e.g. a video on the internal YouTube, an office document from a SharePoint site or news article on the intranet), you will not only see the content itself, but you will also see which other people in the company have seen that content, what tags they gave it, which passages they highlighted or annotated and what rating they gave the piece of content. There are easy ways for you to manage which “social context” you want to see. You can limit it to the people in your direct team, in your personal network or to the experts (either as defined by you or by an algorithm). You love the “aggregated highlights view” where you can see a heat map overlay of the important passages of a document. Another great feature is how you can play back chronologically who looked at each URL (seeing how it spread through the organization).
  • Data enabled meetings
    Just before you go into a meeting you open the invite. Below the title of the meeting and the location you see the list of participants of the meeting. Next to each participant you see which other people in your network they have met with before and which people in your network they have emailed with and how recent those engagements have been. This gives you more context for the meeting. You don’t have to ask the vendor anymore whether your company is already using their product in some other part of the business. The list also jogs your memory: often you vaguely remember speaking to somebody but cannot seem to remember when you spoke and what you spoke about. This tools also gives you easy access to notes on and recordings of past conversations.
  • Automatic “getting-to-know-yous”
    About once a week you get an invite created by “The Connector”. It invites you to get to know a person that you haven’t met before and always picks a convenient time to do it. Each time you and the other invitee accept one of these invites you are both surprised that you have never met before as you operate with similar stakeholders, work in similar topics or have similar challenges. In your settings you have given your preference for face to face meetings, so “The Connector” does not bother you with those video-conferencing sessions that other people seem to like so much.
  • “Train me now!”
    You are in the lobby of the head office waiting for your appointment to arrive. She has just texted you that she will be 10 minutes late as she has been delayed by the traffic. You open the “Train me now!” app and tell it you have 8 minutes to spare. The app looks at the required training that is coming up for you, at the expiration dates of your certificates and at your current projects and interests. It also looks at the most popular pieces of learning content in the company and checks to see if any of your peers have recommended something to you (actually it also sees if they have recommended it to somebody else, because the algorithm has learned that this is a useful signal too), it eliminates anything that is longer than 8 minutes, anything that you have looked at before (and haven’t marked as something that could be shown again to you) and anything from a content provider that is on your blacklist. This all happens in a fraction of a second after which it presents you with a shortlist of videos for you to watch. The fact that you chose the second pick instead of the first is of course something that will get fed back into the system to make an even better recommendation next time.
  • Using micro formats for CVs
    The way that a simple structured data format has been used to capture all CVs in the central HR management system in combination with the API that was put on top of it has allowed a wealth of applications for this structured data.

There are three more titles that I wanted to do, but did not have the chance to do yet.

  • Using external information inside the company
  • Suggested learning groups to self-organize
  • Linking performance data to learning excellence

Book: Head First Data Analytics

I have always been intrigued by O’Reilly’s Head First series of books. I don’t know any other publisher who is that explicit about how their books try to implement research based good practices like an informal style, repetition and the use of visuals. So when I encountered Data Analysis in the series I decided to give it a go. I wrote the following review on Goodreads:

The “Head First” series has a refreshing ambition: to create books that help people learn. They try to do this by following a set of evidence-based learning principles. Things like repetition, visual information and practice are all incorporated into the book. This good introduction to data analysis, in the end only scratches the surface and was a bit too simplistic for my taste. I liked the refreshers around hypothesis testing, solver optimisation in Excel, simple linear regression, cleaning up data and visualisation. The best thing about the book is how it introduced me to the open source multi-platform statistical package “R”.

Learning impact measurement and Knowledge Advisers

The day before Learning Technologies, Bersin and KnowledgeAdvisors organized a seminar about measuring the impact of learning. David Mallon, analyst at Bersin, presented their High-Impact Measurement framework.

Bersin High-Impact Measurement Framework

Bersin High-Impact Measurement Framework

The thing that I thought was interesting was how the maturity of your measurement strategy is basically a function of how much your learning organization has moved towards performance consulting. How can you measure business impact if your planning and gap analysis isn’t close to the business?

Jeffrey Berk from KnowledgeAdvisors then tried to show how their Metrics that Matter product allows measurement and then dashboarding around all the parts of the Bersin framework. They basically do this by asking participants to fill in surveys after they have attended any kind of learning event. Their name for these surveys is “smart sheets” (an much improved iteration of the familiar “happy sheets”). KnowledgeAdvisors has a complete software as a service based infrastructure for sending out these digital surveys and collating the results. Because they have all this data they can benchmark your scores against yourself or against their other customers (in aggregate of course). They have done all the sensible statistics for you, so you don’t have to filter out the bias on self-reporting or think about cultural differences in the way people respond to these surveys. Another thing you can do is pull in real business data (think things like sales volumes). By doing some fancy regression analysis it is then possible to see what part of the improvement can be attributed with some level of confidence to the learning intervention, allowing you to calculate return on investment (ROI) for the learning programs.

All in all I was quite impressed with the toolset that they can provide and I do think they will probably serve a genuine need for many businesses.

The best question of the day came from Charles Jennings who pointed out to David Mallon that his talk had referred to the increasing importance of learning on the job and informal learning, but that the learning measurement framework only addresses measurement strategies for top-down and formal learning. Why was that the case? Unfortunately I cannot remember Mallon’s answer (which probably does say something about the quality or relevance of it!)

Experimenting with Needlebase, R, Google charts, Gephi and ManyEyes

The first tool that I tried out this week was Needlebase. This tool allows you to create a data model by defining the nodes in the model and their relations. Then you can train it on a web page of your choice to teach it how to scrape the information from the page. Once you have done that Needlebase will go out to collect all the information and will display it in a way that allows you to sort and graph the information. Watch this video to get a better idea of how this works:

I decided to see if I could use Needlebase to get some insights into resources on Delicious that are tagged with the “lak11″ tag. Once you understands how it works, it only takes about 10 minutes to create the model and start scraping the page.

I wanted to get answers to the following questions:

  • Which five users have added the most links and what is the distribution of links over users?
  • Which twenty links were added the most with a “lak11″ tag?
  • Which twenty links with a “lak11″ tag are the most popular on Delicious?
  • Can the tags be put into a tag cloud based on the frequency of their use?
  • In which week were the Delicious users the most active when it came to bookmarking “lak11″ resources?
  • Imagine that the answers to the questions above would be all somebody were able to see about this Knowledge and Learning Analytics course. Would they get a relatively balanced idea about the key topics, resources and people related to the course? What are some of the key things that would they would miss?

Unfortunately after I had done all the machine learning (and had written the above) I learned that Delicious explicitly blocks Needlebase from accessing the site. I therefore had to switch plans.

The Twapperkeeper service keeps a copy of all the tweets with a particular tag (Twitter itself only gives access to the last two weeks of messages through its search interface). I manage to train Needlebase to scrape all the tweets, the username, URL to user picture and userid of the person adding the tweet, who the tweet was a reply to, the unique ID of the tweet, the longitude and latitude, the client that was used and the date of the tweet.

I had to change my questions too:

Another great resource that I re-encountered in these weeks of the course was the Rosling’s Gapminder project:

Google has acquired some part of that technology and thus allows a similar kind of visualization with their spreadsheet data. What makes the data smart is the way that it shows three variables (x-axis, y-axis and size of the bubble and how they change over time. I thought hard about how I could use the Twitter data in this way, but couldn’t find anything sensible. I still wanted to play with the visualization. So at the World Bank’s Open Data Initiative I could download data about population size, investment in education and unemployment figures for a set of countries per year (they have a nice iPhone app too). When I loaded that data I got the following result:

Click to be able to play the motion graph

Click to be able to play the motion graph

The last tool I installed and took a look at was Gephi. I first used SNAPP on the forums of week and exported that data into an XML based format. I then loaded that in Gephi and could play around a bit:

Week 1 forum relations in Gephi

Week 1 forum relations in Gephi

My participation in numbers

I will have to add up my participation for the two (to three) weeks, so in week 3 and week 4 of the course I did 6 Moodle posts, tweeted 3 times about Lak11, wrote 1 blogpost and saved 49 bookmarks to Diigo.

The hours that I have played with all the different tools mentioned above are not mentioned in my self-measurement. However, I did really enjoy playing with these tools and learned a lot of new things.

Notes and Reflections on Day 2 and 3 of I-KNOW 2010

I-KNOW 2010

I-KNOW 2010

These are my notes and reflections for the second and third days of the 10th International Conference on Knowledge Management and Knowledge Technologies (I-KNOW 2010).

Another appstore!
Rafael Sidi from Elsevier kicked of the second day with a talk titled “Bring in ‘da Developers, Bring in ‘da Apps – Developing Search and Discovery Solutions Using Scientific Content APIs” (the slightly ludicrous title was fashioned after this).

He opened his talk with this Steve Ballmer video which, if I was the CIO of any company, would seriously make me reconsider my customer relationship with Microsoft:

(If you enjoyed that video, make sure you watch this one too, first watch it with the sound turned off and only then with the sound on).

Sidi is responsible for Elservier’s SciVerse platform. He has seen that data platforms are increasingly important, that there is an explosion of applications and that people work in communities of innovation. He used Data.gov as an example: it went from 47 sources to 220,000+ sources within a year’s time and has led to initiatives like Apps for America. We need to have an “Apps for science” too. Our current scientific platforms make us spend too much time gathering instead of analysing information and none of them really understand the user’s intent.

The key trends that he sees on the web are:

  • Openness and interoperability (“give me your data, my way”). Access to APIs helps to create an ecosystem.
  • Personalization (“know what I want and deliver results on my interest”). Well known examples are: Amazon, Netflix and Last.fm
  • Collaboration & trusted views (“the right contacts at the right time”). Filtering content through people you trust. “Show me the articles I’ve read and show me what my friends have right differently from me”. This is not done a lot. Sidi didn’t mention this but I think things like Facebook’s open API are starting to deliver this.

So Elsevier has decided to turn SciVerse, the portal to their content, into a platform by creating an API with which developers can create applications. Very similar to Apple’s appstore this will include a revenue sharing model. They will also nurture a developers community (bootstrapping it with a couple of challenges).

He then demonstrated how applications would be able to augment SciVerse search results, either by doing smart things with the data in a sidebar (based on aggregated information about the search result) or by modifying a single search result itself. I thought it looked quite impressive and thought it was a very smart move: scientific publishers seem to be under a lot of pressure from things like Open Access and have been struggling to demonstrate their added value in this Internet world. This could be one way to add value. The reaction from the audience was quite tough (something Sidi already preempted by showing an “I hate Elsevier”-tweet in his slides). One audience member: “Elsevier already knows how to exploit the labour of scientists and now wants to exploit the labour of developers too”. I am no big fan of large publisher houses, but thought this was a bit harsh.

Knowledge Visualization
Wolfgang Kienreich demoed some of the knowledge visualization products that the Know-Center has developed over the years. The 3D knowledge space is not available through the web (it is licensed to a German encyclopedia publisher), but showed what is possible if you think hard about how a user should be able to navigate through large knowledge collections. Their work for the Austrian Press Agency is available online in a “labs” evironment. It demonstrates a way of using faceted search in combination simple but insightful visualizations. The following example is a screenshot showing which Austrian politicians have said something about pensions.

APA labs faceted visual search

APA labs faceted visual search

I have only learned through writing this blog post that Wolfgang is interested in the Prisoner’s Dilemma. I would have loved to have talked to him about Goffman’s Expression games and what they could mean for the ways decisions get made in large corporations. I will keep that for a next meeting.

Knowledge Work
This track was supposed to have four talks, but one speaker did not make it to the conference, so there were three talks left.

The first one was provocatively titled “Does knowledge worker productivity really matter?” by Rainer Erne. It was Drucker who said that is used to be the job of management to increase the productivity of manual labour and that is now the job of management to make knowledge workers more productive. In one sense Drucker was definitely right: the demand for knowledge work is increasing all the time, whereas the demand for routine activities are always going down.

Erne’s study focuses on one particular part of knowledge workers: expert work which is judgement oriented, highly reliant on individual expertise and experience and dependent on star performance. He looked at five business segments (hardware development, software development, consulting, medical work and university work) and consistently found the same five key performance indicators:

  • business development
  • skill development
  • quality of interaction
  • organisation of work
  • quality of results

This leads Erne to belief that we need to redefine productivity for knowledge workers. There shouldn’t just be a focus on quantity of the output, but more on the quality of the output. So what can managers do knowing this? They can help their experts by being a filter, or by concentrating their work for them.

This talk left me with some questions. I am not sure whether it is possible to make this distinction between quantitative and qualitative output, especially not in commercial settings. The talk also did not address what I consider to be the main challenge for management in this information age: the fact that a very good manual worker can only be 2 or maybe 3 times as productive as an average manual worker, whereas a good knowledge worker can be hundreds if not thousands times more productive than the average worker.

Robert Woitsch talk was titled “Industrialisation of Knowledge Work, Business and Knowledge Alignment” and I have to admit that I found it very hard to contextualize what he was saying into something that had any meaning to me. I did think it was interesting that he really went in another direction compared to Erne as Woitsch does consider knowledge work to be a production process: people have to do things in efficient ways. I guess it is important to better define what it is we actually mean when we talk about knowledge work. His sites are here: http://promote.boc-eu.com and http://www.openmodels.at.

Finally Olaf Grebner from SAP research talked about “Optimization of Knowledge Work in the Public Sector by Means of Digital Metaphors”. SAP has a case management system that is used by organisations as a replacement for their paper based system. The main difference between current iterations of digital systems and traditional paper based systems is that the latter allows links between the formal case and the informal aspects around the case (e.g. a post-it note on a case-file). Digital case management systems don’t allow informal information to be stored.

So Grebner set out to design an add-on to the digital system that would link informal with formal information and would do this by using digital metaphors. He implemented digital post-it notes, cabinets and ways of search and his initial results are quite positive.

Personally I am bit sceptical about this approach. Digital metaphors have served us well in the past, but are also the cause for the fact that I have to store my files in folders and that each file can only be stored in one folder. Don’t you lose the ability to truly re-invent what a digital case-management system can do for a company if you focus on translating the paper world into digital form? People didn’t like the new digital system (that is why Grebner was commissioned to do make his prototype I imagine). I believe that is because it didn’t allow the same affordances as the paper based world. Why not focus on that first?

Graz Kunsthaus, photo by Marion Schneider & Christoph Aistleitner, CC-licensed

Graz Kunsthaus, photo by Marion Schneider & Christoph Aistleitner, CC-licensed

Knowledge Management and Learning
This track had three learning related sessions.

Martin Wolpers from the Fraunhofer Institute for Applied Information Technology (FIT) talked about the “Early Experiences with Responsive Open Learning Environments”. He first defined each of the terms in Responsive Open Learning Environments:
Responsive: responsiveness to learners’ activities in respect to learning goals
Open: openness for new configurations, new contents and new users
Learning Environment: the conglomerate of tools that bring together people and content artifacts in learning activities to support them in constructing and processing information and knowledge.

The current generation of Virtual Learning Environments and Learning Management Systems have a couple of problems:

  • Lack of information about the user across learning systems and learning contexts (i.e. what happens to the learning history of a person when they switch to a different company?)
  • Learners cannot choose their own learning services
  • Lack of support for open and flexible personalized contextualized learning approach

Fraunhofer is making an intelligent infrastructure that incorporates widgets and existing VLE/LMS functionality to truly personalize learning. They want to bridge what people use at home with what they use in the corporate environment by “intelligent user driven aggregation”. This includes a technology infrastructure, but also requires a big change in understanding how people actually learn.

They used Shindig as the widget engine and Opensocial as the widget technology. They used this to create an environment with the following characteristics:

  • A widget based environment to enable students to create their own learning environment
  • Development of new widgets should be independent from specific learning platforms
  • Real-time communication between learners, remote inter-widget communication, interoperable data exchange, event broadcasting, etc.

He used a student population in China as the first people to try the system. It didn’t have the uptake that he expected. They soon realised that this was because the students had come to the conclusion that use or non-use of the system did not directly affect their grades. The students also lacked an understanding of the (Western?) concept of a Personal Learning Environment. After this first trial he came to a couple of conclusions. Some where obvious like that you should respect the cultural background of your students or that responsive open learning environments create challenges on the technology and the psycho-pedagogical side. Other were less obvious like that using an organic development process allowed for flexibility and for openly addressing emerging needs and requirements and that it makes sense to enforce your own development to become the standard.

For me this talk highlighted the still significant gap that seems to exist between computer scientists on the one side and social scientists on the other side. Trying out Personal Learning Environments in China is like sending CliniClowns to Africa: not a good idea. Somebody could have told them this in advance, right?

Next up was a talk titled “Utilizing Semantic Web Tools and Technologies for Competency Management” by Valentina Janev from the Serbian Mihajlo Pupin Institute. She does research to help improve the transferability and comparability of competences, skills and qualifications and to make it easier to express core competencies and talents in a standardized machine accessible way. This was another talk that was hard for me to follow because it was completely focused on what needs to happen on the (semantic) technical side without first giving a clear idea of what kind of processes these technological solutions will eventually improve. A couple of snippets that I picked up are that they are replacing data warehouse technologies with semantic web technologies, that they use OntoWiki a semantic wiki application, that RDF is the key word for people in this field and that there is thing called DOAC which has the ambition to make job profiles (and the matching CVs) machine readable.

The final talk in this track was from Joachim Griesbaum who works at the Institute of Information Science and Language Technology. The title of his talk must have been the longest in the conference: “Facilitating collaborative knowledge management and self-directed learning in higher education with the help of social software, Concept and implementation of CollabUni – a social information and communication infrastructure”, but as he said: at least it gives you an idea what it is about (slides of this talk are available here, Griesbaum was one of the few presenters that made it clear where I could find the slides afterwards).

A lot of social software in higher education is used in formal learning. Griesbaum wants to focus on a Knowledge Management approach that primarily supports informal learning. To that end he and his students designed a low cost (there was no budget) system from the bottom up. It is called CollabUni and based on the open source e-portfolio solution (and smart little sister of Moodle) Mahara.

They did a first evaluation of the system in late 2009. There was little self-initiated knowledge activity by the 79 first year students. Roughly one-third of the students see an added value in CollabUni and declare themselves ready for active participation. Even though the knowledge processes that they aimed for don’t seem to be self-initiating and self-supporting, CollabUni still shows and stands for a possible low-cost and bottom-up approach towards developing social software. During the next steps of their roll out they will pay attention to the following:

  • Social design is decisively important
  • Administrative and organizational support components and incentive schemes are needed
  • Appealing content (for example an initial repository of term papers or theses)
  • Identify attractive use cases and applications

Call me a cynic, but if you have to try this hard: why bother? To me this really had the feeling of a technology trying to find a problem, rather than a technology being the solution to the problem. I wonder what the uptake of Facebook is with his students? I did ask him the question and he said that there has not been a lot of research into the use of Facebook in education. I guess that is true, but I am quite convinced there is a lot use of Facebook in education. I believe that if he had really wanted to leverage social software for the informal part of learning, he should have started with what his students are actually using and try to leverage that by designing technology in that context, instead of using another separate system.

Collaborative Innovation Networks (COINs)
The closing keynote of the conference was by Peter A. Gloor who currently works for the MIT Center for Collective Intelligence. Gloor has written a couple of books on how innovation happens in this networked world. Though his story was certainly entertaining I also found it a bit messy: he had an endless list of fascinating examples that in the end supported a message that he could have given in a single slide.

His main point is that large groups of people behave apparently randomly, but that there are patterns that can be analysed at the collective level. These patterns can give you insight into the direction people are moving. One way of reading the collective mind is by doing social network analysis. By combining the wisdom of the crowd with the wisdom of groups of experts (swarms) it is possible to do accurate predictions. One example he gave was how they had used reviews on the Internet Movie Database (the crowd) and on Rotten Tomatoes (the swarm) to predict on the day before a movie opens in the theatres how much the movie will bring in in total.

The process to do these kinds of predictions is as follows:

COIN cycle

COIN cycle

This kind of analysis can be done at a global level (like the movie example), but also in for example organizations by analysing email-archives or equipping people with so called social badges (which I first read about in Honest Signals) which measure who people have contact with and what kind of interaction they are having.

He then went on to talk about what he calls “Collaborative Innovation Networks” (COINs) which you can find around most innovative ideas. People who lead innovation (think Thomas Edison or Tim Berners-Lee) have the following characteristics:

  • There are well connected (they have many “friends”)
  • They have a high degree of interactivity (very responsive)
  • They share to a very high degree

All of these characteristics are easy to measure electronically and thus automatically, so to find COINs you find the people who score high on these points. According to Gloor high-performing organizations work as collaborative innovation networks. Ideas progress from Collaborative Innovation Network (COIN) to Collaborative Learning Network (CLN) to Collaborative Interest Network (CIN).

Twitter is proving to be a very useful tool for this kind of analysis. Doing predictions for movies is relatively easy because people are honest in their feedback. It is much harder for things like stock, because people game the system with their analysis. Twitter can be used (e.g. by searching for “hope”, “fear” and “worry” as indicators for sentiment) as people are honest in their feedback there.

Finally he made a refence in his talk to the Allen curve (the high correlation between physical distance and communication, with a critical distance of 50 meters for technical communication). I am sure this curve is used by many office planners, but Gloor also found an Allen curve for technical companies around his university: it was about 3 miles.

Interesting Encounters
Outside of the sessions I spoke to many interesting people at the conference. Here are a couple (for my own future reference).

It had been a couple of years since I had last seen Peter Sereinigg from act2win. He has stopped being a Moodle partner and now focuses on projects in which he helps global virtual teams in how they communicate with each other. There was one thing that he and I could fully agree on: you first have to build some rapport before you can effectively work together. It seems like such an obvious thing, but for some reason it still doesn’t happen on many occasions.

Twitter allowed me to get in touch with Aldo de Moor. He had read my blog post about day 1 of this conference and suggested one of his articles for further reading about pattern languages (the article refers to a book on a pattern language for communication which looks absolutely fascinating). Aldo is a independent research consultant in the field of Community Informatics. That was interesting to me for two reasons:

  • He is still actively publishing in peer reviewed journals and speaking at conferences, without being affiliated with a highly acclaimed research institute. He has written an interesting blog post about the pros and cons of working this way.
  • I had never heard of this young field of community informatics and it is something I would like to explore further.

I also spent some time with Barend Jan de Jong who works at Wolters Noordhoff. We had some broad-ranging discussions mainly about the publishing field: the book production process and the information technology required to support this, what value a publisher can still add, e-books compared to normal books (he said how a bookcase says something about somebody’s identity, I agreed but said that a digital book related profile is way more accessible than the bookcase in my living room, note to self: start creating parody GoodReads accounts for Dutch politicians), the unclear if not unsustainable business model of the wonderful Guardian news empire and how we both think that O’Reilly is a publisher that seem to have their stuff fully in order.

Puzzling stuff
There were also some things at I-KNOW 2010 that were really from a different world. The keynote on the morning of the 3rd day was perplexing to me. Márta Nagy-Rothengass titled the talk “European ICT Research and Development Supporting the Expansion of Semantic Technologies and Shared Knowledge Management” and opened with a video message of Neelie Kroes talking in very general terms about Europe’s digital agenda. After that Nagy-Rothengass told us that the European Commission will be nearly doubling its investment into ICT to 11 billion Euros, after which she started talking about the “Call 5″ of “FP7″ (apparently that stands for the Seventh Framework Programme), the dates before which people should put their proposals in, the number of proposals received, etc., etc., etc. I am pro-EU, but I am starting to understand why people can make a living advising other people how best to apply for EU grants.

Another puzzling thing was the fact that people like me (with a corporate background) thought that the conference was quite theoretical and academic, whereas the researchers thought everything was very applied (maybe not enough research even!). I guess this shows that there is quite a schism between universities furthering the knowledge in this field and corporations who could benefit from picking the fruits of this knowledge. I hope my attendance at this great conference did its tiny part in bridging this gap.

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