I’m using the Faviki bookmarking service to manage bookmarks related to my “technological literacy” research.

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At last night’s Meeting VI of the Alexandria Web Strategy Discussion Group, convened at New Target in Alexandria, Virginia, Maddie Grant raised the topic of RSS feeds. She’s tried a number of readers and now she uses Google Reader. Still, she complains about the problems with setting up readers and especially the challenges of explaining the process to other users. In her mind, RSS has not lived up to its promise.

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I received an email commenting on Social Networking and Elsevier’s “Grand Challenge” for Knowledge Enhancement in the Life Sciences. I had suggested that networked access to published health science authors would be useful in emergency situations where there is the need for rapid access to high quality health information from many different sources.

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By Dennis D. McDonald

How important are the tags you assign to the things you write and publish online? And how important are tags to your finding useful information online?

I thought about these questions when I read Sarah Perez’ Semantic Tagging with Faviki. Faviki,  a new social bookmarking service, suggests what tags to use based on structured data extracted from Wikipedia into an online database called DBpedia.

Unlike services such as del.icio.us, Faviki-suggested tags incorporate a structured tagging vocabulary that has been created and maintained through a communal effort of experts. For example, Faviki prompts you to use a tag such as “coca-cola” instead of “cocacola.” The former term has been incorporated into the DBpedia database along with references to explicit Wikipedia locations for this reference, and this is the term that is recommended.

It’s easy to see the supposed benefits of such an approach to tagging: consistency in assigning tags should make it easier to share information with other people, and relating tags to a structured area of knowledge should make the tag maintenance process easier to perform for those responsible for updating the tags.

While controlled indexing vocabularies and classifications schemes have existed for as long as indexes, catalogs, and information retrieval systems have existed, the benefits of such controlled vocabularies have been somewhat limited to professional and specialized communities or other organizations that already have a vested interest in standard ways of referring to concepts and ideas. Once authorship and usage extend beyond such communities — which happens very easily online — it’s possible that the advantages of standardization, specialization, and specificity of tags might start to break down as profession- and knowledge-based borders are crossed. 

This is sort of a “chicken and egg” situation where, it seems to me,  you need to understand enough about a discipline in order to make the best possible use of a set of structured tags that relate to that discipline. 

On the other hand, a service like Faviki explicitly relies on a tool (DBpedia) that is based on a cross-disciplinary tool like Wikipedia. That suggests that there could exist an underlying cross-disciplinary framework that makes it simpler to cross over community and discipline based boundaries.

But will this be obvious to casual users of Faviki tags? Maybe, maybe not. But I certainly intend to try it out.

• Copyright (c) 2008 by Dennis D. McDonald 

Today we use the web in many ways. Traditional web sites — “places we go” on the web to do things — still exist. But increasingly, web based transactions also depend on the nature of our online relationships with other people.

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If you haven’t seen it yet, check out corporate IT manager Jim MacLennan’s RSS: Underappreciated Web 2.0 in the Enterprise blog post.

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Lee White and I recently initiated an experiment, described here, that consists of our writing about a specific topic (project management and social media) on our respective blogs. Lee writes a post on his blog, I respond on my blog, then we combine and display the posts and the comments we receive in a single RSS feed.

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Reuters recently released Calais to developers. Calais is a set of software tools and rules that read text and automatically assign various tags based on an analysis of the text. Calais outputs tags in the following categories:

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One of the nice things about modern web standards and publishing technology is the ability we non-programmers now have to rapidly combine data from different web based sources through use of easy to handle scripting tools.

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As of October, 20, 2007, the following category tags have each been assigned to ten or more posts in Dennis McDonald’s Blog:

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