1000-and-one pulldowns

- Image by wocrig via Flickr
Luckily, times have come, where semantic search techniques have found their way to enhance knowledge providing theme portals. Nearly once a week a new knowledge portal with built-in semantic search pops up. They deal with environmental issues, health care, economy etc. These sites are good examples how the vision of a knowledge web is fostered by semantic technologies. Such focused approaches are great showcases for “a” semantic web (even if they are not based on “the” RDF semantic web) in the next few months besides general knowledge portals like Wolfram Alpha.
But the potential of these semantic theme portals is often reduced essentially by their bad usability. You get lost in categories and flags – you get puzzled by pulldowns, mouseovers and embedded hierachies – it’s sometimes a mess out off 1001 functions. You need to understand the underpinning semantic concept to get oriented within these applications – and this is not the goal of the exercise. Search has to be easy.
To show the potential of semantic technologies, we need good examples, which offer good usability. This is a call to everyone to provide such examples.
See my favorites:
- NextBio, a platform that enables life science researchers to search, discover, and share knowledge locked within public and proprietary data
- reegle, the Search Engine for Renewable Energy and Energy Efficiency
- CultureSampo, a Finnish cultural heritage platform for institutional organizations as well as private citizens

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May 12th, 2009 at 8:33 pm
At Cazoodle, we envision building specialized vertical search engines that understands the semantics of Web data.
Our first product is for Apartment Rentals— http://apartments.cazoodle.com providing search over rentals all over the Web, with deep integration with map and street view images.
See how it grades on your usability index:
http://www.cazoodle.com
May 13th, 2009 at 11:30 am
thanks govind!
I like your apartements engine as well as you event engine very much! i get a little lost with the shopping service (in my test only “laptop” as a phrase worked).
maybe you can give us some insights, how your engine collect all this data, and what the semantic approch behind looks like. thanks for joining this thread.