If you’re going to be at the 2009 Semantic Technology Conference in San Jose next week, please stop by on Wednesday afternoon and listen to Richard and me present on integrating taxonomy and folksonomy.  This presentation by Earley & Associates was developed by Stephanie Lemiuex over the last several years, who was originally scheduled to present it.  She has developed four categories describing how taxonomy and folksonomy can be used together, and has collected a wealth of illustrative examples.   Richard is an excellent presenter (he has a Ph.D. in Ontology!); I’m honored to share the dais with him and to have the opportunity to present Stephanie’s fantastic content.  

Here’s the official abstract:

Tagging isn’t new – it’s been around for a dog’s age in internet years.  But in the past few years some fresh ideas and tools have reinvigorated the social tagging world.  These new approaches include an attempt to improve findability through a bit of structure and control.  While the idea of adding control to folksonomy seems like going against the whole selling point of social tagging (flexibility, openness), it is bringing the tagging to a new level, making it more viable for practical use in enterprises.  This session will present hybrid approaches to formal taxonomies and social tagging.  How can they be used in the corporate environment?  What type of content is appropriate for social tagging?  What kind of software is available for the enterprise?  Learn how social tagging is not necessarily anathema to corporate taxonomy programs and how this hybrid approach can bring the best of both worlds: a fresh, up to date taxonomy with the structure needed to improve information findability.
Key Takeaways:

• Folksonomy and taxonomy defined
• Drawbacks of pure social tagging
• Social tagging in the enterprise
• Hybrid taxonomy & folksonomy approaches: Four models

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Depending upon context, “tagging” can mean one of three different things: tagging a document, tagging within a document, or tagging a content object.

Tagging documents.  These days most of us think of tagging as the keywords we put on our documents – like our photos and websites – so that others can find them when they search.  User tags are fine for finding photos in flickr, but for tagging to be effective in business we need to make it systematic, so that we avoid ambiguity and improve search recall and relevance.  So we’re increasingly “mature” in our approaches to tagging: We use taxonomy to organize our terms into classes and to manage the relationships between terms.  We develop thesauri and foreign language equivalents.  We integrate taxonomies and thesauri into search indexes for ECM and site search and SEO.

Tagging within a document.  I got interested in tagging in the early days of XML (back when we spelled it “S-G-M-L”), when we were tagging within documents.  By tagging unstructured content inside documents we could do really sophisticated things – not just multi-channel output.  For example, knowing that a paragraph in a document was a step in a service procedure or that a string of gibberish was a part number let us bring life to that content when we transformed it from markup into an interactive electronic technical manual.  Tagging let us turn books into diagnostic software.

Tagging reusable content objects. As content reuse matured with standards like DITA, organizations had more reusable components, with more people creating them in more departments.  Tagging reusable content objects became essential to actually reusing them – if you couldn’t find it, you’d never reuse it.  If you had a single service manual with 100 procedures, now you have at least 100 reusable content objects, so the search scope increased by two orders of magnitude.  At IBM, colleagues report having over a million DITA topics in more than six repositories, with over a dozen departments sharing content across thousands of publications.  Searching for content objects is like trying to find a needle in a haystack, except you’re trying to find the right needle, and you have more and smaller needles to search amongst, in more and increasingly bigger haystacks.

Measuring Metadata Maturity.  Each type of tagging can have measurable benefits on your business.  Five years ago, Earley & Associates and Taxonomy Strategies developed a survey to understand metadata maturity for various types of businesses.  Earley is conducting an updated survey to see how organizations have moved up the learning curve.  Since we have a baseline of responses from five years ago, we’ll be able to describe how metadata and taxonomy practices have matured over time.  Also, the original survey was focused on the impact of metadata best practices on knowledge management and e-commerce search.  We now recognize that metadata is also used by technical communicators – especially those that use XML and other technologies to create, manage, and multichannel publish reusable content.  We want to hear from you all for the first time.

The survey is pretty detailed, so you might want to grab your favorite caffeinated beverage before you dig in.  As compensation for your time (about 15 minutes) Earley & Associates is offering these nifty incentives:

• A free pass to any future Earley & Associates Community of Practice conference call (a $50 value).  These are monthly, and the next one is Wednesday June 2^nd on Taxonomy for Portals featuring Giovanni Piazza, Chief Knowledge Officer of Ernst & Young, and Ralph Poole of Earley & Associates.
• A $200 discount on registration to the Henry Stewart conference on digital asset management, June 1-2 in NYC.  Seth Earley will be there presenting preliminary results.
• Free participation in a webcast reviewing the results of the survey (date TBA).

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Google Squared is Google’s answer (or perhaps one of their answers) to semantic search and Linked Data.  ‘Squared gets its moniker from the matrix used for displaying results – each “square” in the matrix contains some fact derived from the content on the source site. 
Each row in the Google Squared matrix is a search result, but the interesting part is the columns.  ‘Squared relies on RDFa and microformats on the source sites to extract structure for the search category – if it’s available (I’m not entirely sure how ‘Squared derives its semantic structure in the absence of metadata, but clearly it does).  So a search on “rollercoasters” will generate columns for height, speed, construction, etc.  Essentially, ‘Squared is generating search facets on the fly using structure that is implied or explicit in the set results set. 

Because sites are inconsistent with the amount of structure they provide, ‘Squared can – and will – make errors in interpreting free text.  For example, in the video, we can see that “height” – while intended to describe the height of the rollercoaster – sometimes returns text about the minimum height requirements for riders.  Still, from the demo ‘Squared does look interesting.  Predictable categories like Restaurants (e.g. a search on “pizza”) have the dimensions you’d expect to see in columns – description, address, price range, ambiance, etc. 

Google Squared affords general-purpose faceted search, because the columns can be used to refine the search results.  The current alpha doesn’t let you sort on a column, but clearly this is where things are heading.

Google Squared will be made publicly available on the Google Labs site in the next week or so. 

Here is the video courtesy of TechCrunch (over six minutes and shaky-cam – but you’ll get the idea).

As a footnote, TechCrunch declares that ‘Squared “is how Google will crush Wolfram|Alpha”.  I’m sorry – they missed on that point by a country mile.  Alpha isn’t a search engine – it’s a user experience (and a cool one at that) built atop a “curated” database, designed to answer queries that are primarily computational in nature.  Google searches the web.  No comparison – the products aren’t in the same class, and don’t solve the same problem.  Google can’t plot the Julia Set for you – Alpha can.  That doesn’t mean Alpha “crushes” ‘Squared, either.  Geez.   Anyway, more on Alpha later.

Several things were important to me about how to do this:

1. I wanted to use this feature to notify about interesting content without a lot of authoring on my part.  Ideally posting an item to the digest would be automated with a semantic search bot pulling in the content.  Worst case I would have to drag and drop headlines and links in a Twitter-like fashion.
2. I didn’t want to add commentary – I wanted a simple re-blog capability.
3. I wanted attribution of the author to be very clear – this was not my content, just items of interest to share.

Inline RSS for a WordPress page

I concluded that what I really needed was a way to display RSS feeds in-line on a WordPress page to create my headlines or digest.  WordPress has loads of plug-ins that are great for integrating RSS feeds into sidebar widgets, but I found one plug-in – DGE inlineRSS – that provided all of the features I needed to put the RSS into the body of a WordPress posting or page. 

inlineRSS is pretty simple to use.  After install, there are three easy steps to getting a feed embedded into a page.  First, you need to configure inlineRSS to point to your feeds.  This is a simple matter of entering the RSS feed URL, and associating an XSLT file that will transform the feed into the HTML that displays on your site (inlineRSS provides a simple XSLT file that you can alter to meet your needs).  Next, you need to be sure that the configuration sets the path to the XSLT file on your site, and make any changes to the XSLT for your unique formatting.  Lastly, you need to enter the embed code for the inline RSS itself – this is simply:

!inlineRSS:myrssfeed

where myrssfeed is any of the feeds you configured in the inlineRSS options screen.  inlineRSS implements a WordPress filter to replace this code with the XSLT-formatted RSS feed.

Creating the Digest RSS Feed

The next step to getting a digest page up and running was to create the source of the RSS feed itself.  I considered three options:

• reFeed – a server for creating custom RSS feeds from items of interest
• Twitter or Ping.fm – generate a “tweetstream” of headlines and links (re-blogging vs. micro-blogging)
• TextWise – using a semantic search bot (specifically TextWise’s Gyzork demonstration app) to auto-generate a custom RSS feed that contains blog posts and news items that match specific semantic signatures

reFeed.  Last month, Mike Axelrod set up a reFeed server that he and I experimented with briefly.  This may hold some promise for the future for hand-selecting items of interest to reblog via an RSS feed.  However, Mike noted some technical issues, particularly with the quality of the RSS, so we tabled that project for the time being (he and I promise to blog more about reFeed in the near future).

Twitter and Ping.fm.  As Mike discusses in a related post, we’ve both been exploring Twitter and Ping.fm to stream reblog-type items to our websites.  Mike has the Ping.fm custom URL plug-in for WordPress working and has his “pingstream” going to headline items in his sidebar.  I’m doing the same, using Ping to drive Twitter, then putting the Twitter feed into my sidebar with the  Twitter Tools plug-in (look under Headlines in the sidebar to your right).  I’m still working on getting my Ping feed to display in-line with RSS or other techiques.

TextWise Gyzork.  You may be familiar with TextWise if only from my use of their technology here for finding related products, Wikipedia articles, and blogs for my blog posts.  TextWise uses semantic analysis to search the web for similar documents.  Each document (web page, blog post, etc.) gets a semantic signature that identifies the concepts in the document and their weight (i.e. relevance).  Gyzork lets me apply a semantic signature to any document, then create a saved semantic search that returns either blog posts or news items that match.  Gyzork also lets me create an RSS feed for that search.  If you check out my Headlines tab above, you’ll see a set of headlines (embedded using inlineRSS) that were generated by a Gyzork search seeded by one of my posts.  This search has been pretty good over the last six months at surfacing items I’m interested in.

Next Steps

I plan to continue to experiment with the automatic news digest.  Things I plan to try out:

• Propagating the news feed items to my status updates in LinkedIn and Twitter
• Getting my Ping.fm stream to display inline as a set of headlines
• Micro-blogging using Ping as the front end
• Working with TextWise technology to create more Gyzork feeds for the news page
• Cleaning up the XSLT to better format the news feed
• Experiment with other semantic technologies to create automatic feeds of interest

If you have any further ideas on this, please comment.

The Talis Connected Commons scheme is intended to directly support the publishing and reuse of Linked Data in the public domain by removing the costs associated with those activities.

The scheme is intended to support a wide range of different forms of data publishing. For example scientific researchers seeking to share their research data; dissemination of public domain data from a variety of different charitable, public sector or volunteer organizations; open data enthusiasts compiling data sets to be shared with the web community.

For qualifying data sets, Talis will provide, through the Talis Platform:

• Free hosting of up to 50 million RDF triples and 10Gb of content
• Access to data access services that operate on that data, including data retrieval and text search
• Free access to a public SPARQL endpoint for each dataset.

This means that data set providers will not incur any of the commercial costs normally associated with hosting data on the Talis Platform. In addition neither the data set provider or its users will incur any usage charges relating to the use of the Platform services made available on that data.

To qualify for entry into the scheme all data and content hosted in the Platform must be made available under one of the following public domain data licenses:

• Open Data Commons Public Domain Dedication and License
• Creative Commons CC0

As Mike Axelrod and I have been actively discussing, as more of these services become available through web APIs (e.g. Nova Spivak’s hosted ontlogy service, Amazon’s Public Data Sets, or text analysis services like TextWise, OpenCalais, or Amplify), developers can start mashing them up into useful virtual applications.  Marshall Kirkpatrick at ReadWriteWeb discussed the roadmap for this in a recent post. 

First, massive bodies of data are created or gathered, books are scanned, census data is collected, and patients donate their anonymous aggregate medical data to science. Next, the data is semantically analyzed and marked up (through any number of different semantic processing engines). Then, the data is stored and an API is made available (this is where the Talis Connected Commons comes in). Finally, developers build applications that leverage the smart data offered up through the platform, data visualizers find new stories to tell in images built from the marked up data and new relationships between people, organizations and concepts have the mist cleared away from them through systematic analysis of various permutations of previously unavailable structured data.

That last bit is what has Mike and me interested – finding new ways of making use of the relationships between data and content that all the various semantic tools unearth.

This week in Read Write Web, Phil Glockner writes about a personal test drive of the latest version of Glue with the founder of Adaptive Blue, Alex Iskold. 

Below is an excerpt about two of the new features on Glue – connected conversations (which transcend sites), and Smart Recommendations.

Connected Conversations

Building on the concept of being able to share thoughts and opinions on things with your friends on Glue, regardless of the site those things are found on, is taken to the logical next step with the addition of conversations. Now, if you see that someone has commented on something that you are looking at, or have an opinion on, you can add a comment to their opinion. In turn they can comment back, or others can join in on the conversation. Through these interactions, you will be exposed to new people who perhaps came to the conversation from a completely different web site, Wikipedia for instance, instead of Amazon, but are using Glue to transcend the social boundaries of these sites

Smart Recommendations

Being a contextual network that uses semantic technology to gather information and trends, Glue now aggregates this data and can present what books, movies and music your friends like the most instantly. Creating this recommendation data is done automatically as people use the Glue application by indicating what they like. The lesson here is, the more you use Glue, the better a resource you become to your friends who also use the service.

Read the full article here.

Nova Spivack’s semantic web company Twine is developing a free service to write and host semantic ontologies; the classification trees that enable machines to put concepts in topical context. Ready to play Aristotle and create an ontology of cheese, model airplanes, global anti-hunger organizations or any other topic?

What blogging was to publishing, a simple tool that made far more people able to participate, Twine’s new ontology writing and hosting service could be to the act of teaching machines about new topics.

The company wouldn’t let us publish the new service’s name but says it is aiming for a launch date this year, as soon as a go-to-market strategy and appropriate partnerships are lined up. The ontologies created won’t only work on Twine; they will be referenceable by semantic apps anywhere around the web.

A service for authoring and hosting ontologies certainly adds value.  My questions are:

• Who will write these public ontologies?
• When will Twine fix their dreadful user experience?

Mike Axelrod and I have spent a lot of time lately speculating what could be done by mashing-up various semantic web services like Twine’s.  It seem all of the technology providers have “one trick ponies” – you can get entity extraction, sentiment analysis, ontology management, tagging, related content – but what if you want to use multiple technologies?

The Content Guy is pleased to have been invited by Earley & Associates to present in their Semantic Technologies Jumpstart series this fall.  This free series runs every Thursday, from October 30th – November 20th, between 11:30 am – 1:00 pm ET. 

Join us November 6, 2008:

Implementing Semantic Search
Presented by Paul Wlodarczyk and Amber Swope
Semantic search helps business people find answers to pressing questions by wading through oceans of information to find nuggets of meaningful information.  In this presentation we’ll discuss how semantic search and content analysis technologies are starting to appear in the marketplace today.  We’ll provide a recap of what semantic search is and what the key benefits are, then we’ll answer the following questions:
• Is semantic search a feature, an application, or enterprise system?
• How can I add semantic search to my existing work processes?
• Will I need to replace my existing content technologies?
• What will I need to do to prepare my content for semantic search?
• Is semantic search just for documents or can I search my data too?
• Can I use semantic search to find information on the internet and other public data sources?
• Are there standards to consider?

This conference call is for implementers and decision makers of all technical levels including those new to semantic technologies.  We will introduce technical concepts in terms everyone can understand.

For more information on the other presentations in the Semantic Technologies Jumpstart series or to register click here.

There were three separate projects in the integrations with the following goals:

1. To expand the number of queries for which Live Search shows Answers (using data from Freebase). 
2. To use Powerset’s semantic technology to generate improved captions for Wikipedia articles (shown below)
3. To use Powerset’s Factz extraction to generate a list of related searches for a set of queries

While the impact of Powerset’s semantic processing on Live Search is still limited, you can see the beginnings of it (that is, if you’re lucky enough to be in the random sample of searches in the experiment): semantically improved summaries in the captions for results that point to Wikipedia articles.

The integration with Live Search is two-way: Powerset.com now boasts related searches that are powered by Live Search.