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Yesterday I attended the Social Web incubator Bar Camp of the W3C, focusing on issues in web support for social media.   It was a small group, overall, but an interesting group, including folks keen on issues like technical underpinnings (discussion of FOAF, RDF, etc), and folks with an interest in more applied topics like enterprise, health, and journalism.

The issue on the table is what sorts of standards might be necessary or desirable to support social networking on the web in interoperable ways.   One statement that resonated was a comparison between the social web and social networks as being analogous to open space versus silos.   As a general rule, if someone can lock you into their proprietary approach, you are subject to their whims.   If, instead, there are open standards, you’re free to approach things in different ways. For example, email took off once one email standard took hold and allowed different systems to interoperate.   On the other hand, proprietary standards may provide the capital and motivation necessary to invest in the development of advanced features (e.g. the ability of Linden Labs to continue to expand Second Life capabilities as they grabbed market share).

The internet as an open standard (e.g.TCP/IP) has allowed for the development of other standards on top.   If not for the http standard, we wouldn’t have have the world wide web.   However, continued development is needed to meet new needs.   So, for example the Salmon project was represented, which is trying to make a mechanism whereby any   comment on a piece of web content, regardless of location and tool (e.g. blogging about someone’s Flickr picture) could be aggregated back to the original content to maintain the discussion.

This can be real propeller-head stuff, e.g. it was admitted that RDF’s uptake has been hampered by a difficult syntax.   Even Sir Tim Berners-Lee, responsible for the http protocol, admits that the // in the protocol isn’t necessary, and regrets it.   I no longer can get down in the weeds, but fortunately understand it well enough conceptually to talk intelligently about the requirements and see the opportunities.

And opportunities there are.   The next generation, I believe, so-called web 3.0, is when we move to system-generated content.   The discussions that occurred on pulling together useful information to the benefit of organizations, like adding valuable information as a response to your searches and discussions.   Rules operating on data by description has powerful capabilities, e.g. the way Amazon provides mass customization.   There are entailments, of course; taxonomies and ontologies need governance as do other content activities.

Naturally, some of it was more approachable than the geek speak, such as the fact that social engineering was as important as semantic engineering, for example that clever interface design can mitigate getting users to tag content.   Similarly, problems that can arise from bad behavior may be better solved as cultural issues rather than technical ones.

The folks there were fabulously knowledge, for example a post-meeting request for ontologies around project management and pharmaceuticals were richly answered.   While much of this stuff is still in development, the opportunities are coming, and having the necessary understanding on hand to capitalize on it is important.   Note that these people are working to make this stuff work for all of us.   Truly valuable and much to be appreciated.

My recommendation is to be aware of the possibilities and requirements. While you are likely not quite ready to take advantage of it (and there are already opportunities, seriously), you don’t want to do anything that would subsequently make the opportunities harder to capitalize on.   So look into your content data engineering from a semantic point of view as well, and prepare for some truly awesome capabilities.

One of the proposed benefits of social media is the capture of knowledge that’s shared, taking the tacit and making it explicit.   But really, how do we do this?   I think we need to separate out the real from the ideal.

The underlying premise is that we have an enlightened organization that’s empowering collaboration, communication, problem-solving, innovation, etc (what I’m beginning to term ‘inspiration’ in all senses of the word) by providing a social media infrastructure, learning scaffolding, and a supportive culture.   Now, all these people are sharing, but are we, and can we be, leveraging that knowledge?

The obvious first answer is that by sharing it with others, it’s being leveraged.   If information is shared with the relevant people, it’s been captured for organizational use by being spread appropriately.   That’s great, and far too few organizations are facilitating this in a systematic way.   However, I’m always looking for the optimal outcome: not just the best that is seen, but the best that can be. So how can we go further?

The typical response is using data mining that focuses on semantic content: systematically parsing the discussions, and using powerful semantic tools to attempt to capture, characterize, and leverage information systemically. (Hmm, you could map out the knowledge propositions, and link them into coherent chains and then track those over time to see significant changes, even regularly re-sort to see if different perspectives are changing…oh, sorry, got carried away, enough adaptive system designing :).

In terms of social media systems, while there are analytics available, semantics are not part of it, as far as I can see.   Further, I searched on social media mining, and found out that the first international workshop will be happening in November, but it’s not happened yet. There’s an interesting PhD thesis on the topic from UMaryland, but it’s focused on blogs and recommendations. In other words, it’s not ready for prime time.

The point is, that machine learning and knowledge mining mechanisms are in our future, but not our present.   Don’t get me wrong, there are huge possibilities and opportunities here, but they’re a ways off.   So, are we back to the best that can be?   I want to suggest one other possibility.   The systemic mechanisms are nice because, set up properly, they run regardless, but there’s another approach, and that’s human processing.   For all the advances in technology, our brains are still pretty much the most practical semantic pattern matching engines going.   So how would that work?

Well, let’s go back to the role that learning professionals play. We’ve already looked at how they could change as learning units take over responsibility for the broader picture of learning in the organization.   Learning professionals need to be nurturing social learning, and that means being in there, monitoring discussions for opportunities to draw out other members, spark useful feedback, develop skills, and more.

Well, they also can and should be looking for outcomes that could be redesigned/redeveloped/reproduced for broader dissemination.   They should be monitoring what’s happening and looking for information that’s worth culling out and distilling into something that’ll really bring out the impact of that information. Turning information into knowledge and even wisdom!

Yes, that’s a greater responsibility (though it’s also fun; you shouldn’t be in the learning space if you don’t love learning!).   It’s a new skill set, but I’ve already argued that.   The world’s changing, and the status quo won’t last long anyway.   So, while you can just allow and hope that individuals will perceive the value of the information created, and even facilitate by encouraging people to participate in all the relevant communities (which will likely cross role, product/service, and more), there’s a step further that’s to the benefit of the organization and the learners.

We’ll steadily build support for that process, but it will be facilitated, and advanced, by individual practice to complement, supplement, and inform the mechanistic approaches.   Don’t ignore this role; plan for it, prepare for it, and skill for it.   Responsibility for recognizing should be shared, so that the individuals in the network are also doing it (for example, retweeting valuable information), and that’s a learning skill that should be developed.

Here’s hoping you find this valuable!