An implementation of Semantic MediaWiki in public administration reiceved a jury award yesterday in the final ceremony of the highly coveted multimedia state award (Staatspreis Multimedia) 2008 in Vienna: Centre for Public Administration KDZ’s platform for the cooperation of administrations (Plattform Verwaltungskooperation) in Austria, Germany, Italy and Switzerland received praise for its use of open, semantic technologies in their effort to further the collaboration between administrations and administrative staff. Those of you who can read German: read the response from Bernhard Krabina, KDZ, here or contact him here, if you’d like to learn more. The top state award itself went to HPC Dual, a combination of electronic and physical mail delivery.
Also published yesterday was an interview with Matthias Schindler, former member of board of Wikimedia Germany, at the occasion of the publication of a physical Wikipedia, i.e. a one-volume encyclopedia in print (publisher: Wissen Media, a Bertelsmann division). According to the English Wikipedia, “the volume is planned to include abbreviated entries for the 50,000 most commonly used search terms of the prior two years. The book is to be priced at 19.95 euros, with one euro from every sale going to the German chapter of the Wikimedia Foundation.”
The interviewers also asked Schindler for his “encyclopedic Wikipedia dream” – I hope his response will catch on in the Wikimedia chapters worldwide:
I would one day like to see a large edition of Wikipedia (including a German language edition), which makes use of the Semantic MediaWiki extension. The dream in a nutshell, without consideration of the current state of research and development: A wikipedia that can be read not only by humans, but also by computers, a Wikipedia that can offer concrete answers to concrete questions and that creates content individually for users, something that they can make use of; great if Wikipedia played the role of the first, mainstream Semantic Web application. While this is still in the process of coming together, there are enough other things for us to do.
(btw, my translation).
Concrete answers to concrete questions, a personalized Wikipedia – I am not even aiming that high at the moment.
How often do you think these need an update? And if a new hardcore punk band is added – will the creators of the new article think about adding it to the list? What about articles which make make a reference to or mention things that are or should be on a particular list?
As a list has the inherent claim of being complete, it shouldn’t be left to humans to create and maintain them – leave that to the machines! Vote Semantic MediaWiki for Wikipedia!
What’s it about? Semantifind is an IE and FF browser plug-in that extends Google’s search functionalities, most notably through a typeahead functionality that allows you to refine your search results before hitting ‘enter’. ReadWriteWeb wasn’t too impressed though:
Unfortunately, SemantiFind is one of those tools that’s good in theory, but not so good in practice. When performing some test searches, results were not as precise as they should have been. For example, in the above-mentioned search for “Georgia,” a search for the U.S. state returned Google results for the country as well.
Ambiguities due to homonyms such as GeorgiavsGeorgia, or JavavsJava are among the faves of people who are trying to pitch a semantic tool to you – but I really wonder whether the effects of homonyms aren’t highly overrated? How often do people really search for these, and in particular search for these without context, i.e. further search terms such as in ‘Georgia Tech’, ‘Georgia war’, ‘Java Coffee’ or ‘Java bugs’?
I must say I was quite impressed by the choice of search terms offered, and if you (like me) are easy prey for the serendipity effect, then SemantiFind can please and distract you endlessly. Here is a preview of what appears if you enter ’serendipity’ – please note the preview of possible descriptions and definitions which you get on the Google homepage with the plugin (click > big):
Once you pick a term it turns into a kind of button (just slightly annoying: you cannot edit a term after it’s turned into a button, but would have to delete the whole thing and type again if you want to change your search query):
And then, what happens? On the search results page, you see results filtered by SemantiFind’s user-generated, user-approved labels on top of the other search results – which irritated me at first as it comes across as a search engine within the search engine. Admittedly: I’d rather sift through 13 results than through 10,900,00 search results (even though I never make it to the end of Google’s search list anyway; does anybody?) – but does the article about trees doing their best work with thermostats at 70° really deserve the second rank in SemantiFind’s list of recommended search results?
So while I agree with RWW that this “just goes to show why search engines that rely on people to filter the results might not work. Human error shouldn’t be a factor in web searches”, I am still quite fond of the suggestions and definition previews. I would probably use SemantiFind regularly if they allowed me to configure the plugin in such a way that I’d get the suggestions on the input page, but not the recommended results on the results page.
What’s the source of these results anway? SemantiFind’s recommended results seem to rely entirely on input generated by users – to add input, you need to install their toolbar and start adding labels to websites; if a website has been labeled before, you can confirm or reject existing labels. What’s nice: a label recommender (only presumably the same one that’s used for search queries) reduces ambiguity. What’s curious: You can also browse the pages you have already labeled in what they call your “catalogue” – which makes the service even more reminiscent of a bookmarking service, and which makes me wonder whether one shouldn’t possibly link this with a del.icio.us/Mr.Wong/Bibsonomy/Faviki account (Faviki would probably be the best, considering their tag recommendations are based on DBpedia, and considering that Faviki just added 1 million new tags and now holds more than 5 million tags across all languages)
Questions that remain: I’d really like to know how they maintain their list of suggested labels – ambiguity, typos, plurals forms, i.e. the usual folksonomy issues must be a big challenge. Also, I’d like to know where they get their definitions in the preview from – from Google? Or are these user-generated as well? There must, after all, be some use for the “request a new definition” form?
Too bad they don’t have a blog to which one could send a track back, and there is nothing much on their company page either.
This second day of TRIPLE-I 2008 was my personal folksonomy day, even though the theme was already set yesterday, with Andreas Hotho’s invited talk about “Extracting Semantics from Folksonomies” which was the opening lecture of the workshop “Knowledge acquisition from the Social Web.”
Andreas Hotho is directing the Bibsonomy project at Kassel University’s Knowledge and Data Engineering resarch group; Bibsonomy is a social bookmark and publication sharing system catering especially for researchers who, next to bookmarkingm also wish to manage publications. Next to other interesting things, Bibsonomy supports the import of bookmarks from del.icio.us, Firefox bookmarks and local BibTex files. Being a project led by a university’s computer science department, Bibsonomy is at the same time the result, the object and a stimulus for research in the area of tagging and folksonomies. Andreas describes this double appeal of folksonomies to both ordinary people and researchers in a 12 seconds vlog post:
One of the outcomes of the research into folksonomies is FolkRank, a search algorithm that exploits the structure of folksonomies; the name reveals that it was inspired by PageRank, but as the graph of folksonomy structures does not correspond to the web graph, some adaptations had to be made. The specifics of these adaptations can be found in an online article by Andreas and his colleagues: “FolkRank: A Ranking Algorithm for Folksonomies” (PDF, 268 KB).
Andreas Hotho’s talk more specifically addressed the search for methods to identify tags which describe the same concept (or a more specific / a more general concept respectively) within a folksonomy. He suggested two approaches:
Applying measures directly to folksonomy statistics, allowing to describe tags as a vector; e.g. co-occurrence frequency and FolkRank could serve as a similarity measure (with these two having a tendency towards high-frequency tags) or a cosine method (which is more likely to produce “siblings”)
Looking up tags in an external thesaurus/vocabulary (for instance achieving semantic grounding by mapping a tag and its most similar tags with Wordnet Synsets)
Future areas of interest within folksonomy research Andreas proposed were trend detection, tag recommendation, detecting spam (a major challenge!), logsonomies (i.e. the structure of search engine query log files) and learning synsets, hierarchies, and structures of folksonomies. Andreas Hotho can be contacted via his homepage, if you have any further questions regarding Bibsonomy, FolkRank or this present piece of research.
“Like plants or animals, tags evolve in an emergent fashion, open to hybridisation. Stewardship can help grow and put roots down.
Helping the darwinian process is tag gardening.
Tag gardening is about taking tags in the wild and tending to them, or identifying a wild tag that will do well in your south facing IT
garden. I am talking about domestication here.
Just like there are professional bloggers i am pretty sure some parties will emerge that get paid for their abilities.”
I seriously hope that the latter is going to come true, even though I have the feeling that most providers will continue to consider user input and effort pro bono work!
Katrin Weller’s intro (Isabella Peters had excused herself) focused on the well-known problems with tags and folksonomies, e.g. :
spelling variants, synonyms, abbreviations, different natural languages
adhoc or personal functions of tags other than content description (e.g. “toread”, “@Henry”, “nicepic”)
flatness of tag clouds which allows for browsing by popularity, but not by semantic interrelations
She further distinguished three levels where tag or tag cloud improvement becomes relevant:
single document vs document collection level
Single user vs collaborative level
intra- and cross plattform level (e.g. different tagging conventions, tag separation with comma or blank space, etc)
To push the gardening metaphor even further, Kathrin presented us their ideas of weeding, seeding, fertilizing etc.:
Weeding
The weeds in this case are “bad” tags like spam or misspelled tags (weed: any plant that crowds out cultivated plants)
Aim: enhancing recall and a consistent indexing vocabulary
Achieved by: type-ahead functionality, editing funcionalities, natural language processing, user guidelines for indexing and retrieval, nomination of authorized users as gardeners
Seeding
Seeding in folksonomies means to expand frequently used tags by more specific tags (called “baby tags” or “seedlings” by Katrin Weller; seedling: young plant or tree grown from a seed)
Landscaping
The idea of landscaping here means to create “flower beds” through identifying species of tags, e.g. by similarity.
Aim: enhancing precision and expressiveness
Fertilizing
Fertilizing in this context means to combine folksonomies with other knowledge organization systems (KOS): thesauri, controlled vocabularies, ontologies, etc. (fertilizer: any substance such as manure or a mixture of nitrates used to make soil more fertile). Fertilizing might work both ways, Katrin suggested: a folksonomy might be fertilized with the semantic structure of a KOS, or a KOS enhanced by terms from a folksonomy.
And finally TagCare: The ambitious plan is to have a system that allows to import tag clouds from Flickr, deli.icio.us and Bibsonomy, cleanse out dissimilarities between tags, add hierarchical structure to the tag clouds, allow the user to view tag statistics and probably also to have community features, such calibrating one’s tags with those of the chief gardener or to activate collaborative spam elimination. It is going to be a free service, and if you want to be notified when it goes live, you might want to send an email to Katrin.
This full-service proposal for tag gardening does of course sound brilliant – yet is it going to be feasible, on a technical level? In the post-presentation discussion, somebody mentioned Faviki, which relies on DBpedia concepts to solidify the tag cloud. It didn’t exactly seem as though the TagCare team had already thought along these (semantic web) lines, even though this perfectly corresponded to their ‘Fertilizing’ idea. But if TagCare solely relies on good human gardeners, how long will it take until they have gained a big enough community to stimulate someone’s altruism? The idea of tag gardening of course is beautiful, and I am curious to learn more about the technology it is going to use.
Other folksonomy and tag related presentations that I was unable to attend or am unable to describe now, after the 10th hour of my 2nd day at TRIPLE-I, with a band performing folkore music involving yodeling and probably Schuhplattler right outside of this room:
Providing Multi Source Tag Recommendations in a Social Resource Sharing Platform (Martin Memmel, Michael Kockler, Rafael Schirru, German Research Centre for Artificial Intelligence DFKI)
Semantic Tagging and Inference in Online Communities, Yildirim Ahmet, Üsküdarli Suzan, Boğaziçi University
Using Visual Features to Improve Tag Suggestions in Image Sharing Sites (Mathias Lux, Oge Marques, Arthur Pitman, Klagenfurt University)
Harnessing Wikipedia for Smart Tags Clustering (Maria Grineva, Maxim Grinev, Denis Turdakov, Pavel Velikhov, Russian Academy of Sciences)
Please leave a comment if you think that any of the above needs correction.
EDIT: I got the chance to record another 12 seconds definition (and am thinking of setting up a video glossary for the Semantic Web now): Rolf Sint from Salzburg Research explains what folksonomies are and why folksonomies and ontologies go together well in 12 seconds! Rolf is also involved in the KiWi project, which aims to develop a wiki-based knowledge management system boosted by semantic technologies.
Someone wondered why I was blogging so intensely about the KiWi Project Kick-off: Not only because it is an intriguing project but also, yes indeed, because the Semantic Web Company itself is part of the KiWi Project, and my blogging was not simply the result of some arbitrary interest:-) The main two work packages to which SWC is contributing are Application building (WP6), i.e. the use case scenario which is going to be elaborated in collaboration with Sun Microsystems, and Demonstration (WP9), in particular the Technology Road Show where the project outcomes are going to be presented and demonstrated on-site to potentially interested organisations. I am also going to be involved in Demonstration, but first and foremost in Dissemination (WP8), which is the third biggest work package, and which aims at spreading the word in the scientific community.
But who is the Semantic Web Company (SWC) anyway and what to they do? (more…)
Sweet Tools is a comprehensive collection of tools and applications for the Semantic Web. It is maintained by Mike Bergman with help from the Semantic Web Company. [more]