Monrai Blog

News about Cypher, Semantic Web, Natural Language Processing, and Computational Linguistics

Saturday, May 30, 2009

Razorbase vs. Parallax

For those who don't know, David Huynh, most noted for his work at the Similie Project, released a faceted browser for Freebase (now part of the LOD dataset) earlier this year. Much of my work on facets/set-based browsing is based on his. Here's a compare/contrast (see presentation below) of his Parallax browser with razorbase that may be useful.

There are now two new buttons/actions that came out of earlier observations of how people interact with the razorbase UI: mutual connections and descendants.

Mutual Connections: allows you to view the mutual connection of a certain type linked to the subject, with one click. E.g., if you were viewing Someone's friends, this button will take you to their friends.

Descendants: allows you to view the descendants of a connection. If you were viewing People who influenced someone, this button will take you to people who they influenced. Use to pull friend of a friend, a person's ancestry, etc.

Initial experiences:
I've found that what folks discover is most useful about the service is the ability to develop their own strategy for finding the information they need. By refining my criteria through trial and error browsing, I was able to find valuable Web resources about an esoteric research topic: "Recommendation Engines/technology". The results were several orders of magnitude more precise than Google and Wikipedia yielded.

I began my looking for things named Recommendation Engines, which returned all things with that title in its name. I then drilled into the Documents and Articles category. From there, I examined each article in the list, and manually collected a list of companies that I found to interest me. After pulling a list of companies that develop said technology, I was able to go to the Websites that published stories about those companies, e.g. by looking up Companies named riffs.com. Using the mutual connections button, I found the other stories published by those sites (because in most cases, the site containing the story were sites dealing specifically about my topic). From there, I figured that those stories probably contain links to stories/companies/web services related to my topic, so using the Information Explorer, I pulled all links referenced by those documents, and got a great list that yielded more companies in that space. Then I figured, the links in those documents may also be related. The descendant button allowed me to pull the links two-degrees out from the original list, yielding a list that was less relevant, but which did contain a few precious nuggets. In the poverty of data regarding my esoteric topic, the ability to locate those few nuggets by 1) defining a criteria based on Category and other information about my topic, and 2) drilling through and cutting the results, delivered value that I truly can not find anywhere else on the Web. This was my first real-world experience with the value proposition of the linked data web. The resulting presentation was one which I would not have been able to compile otherwise within the time constraints I was given.

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