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Commodity Semantic Search:  A Case Study of DiscoverEd Nathan R. Yergler Creative Commons Semantic Technology Conference 24 June 2010
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CC Rights Expression Language
CC licenses are based on international copyright law
There are hundreds of millions of pieces of CC-licensed content on the web
OER ,[object Object]
Learning materials that are freely available to use, remix, and redistribute.
Wide variety of format, content types, audience
CC licenses make this content interoperable
But how do you find OER you’re looking for?
 
OER Search == CC Search++ ,[object Object]
It's up to publishers to label their works ,[object Object],[object Object]
Additional facets – subject, language, etc
A Model for OER Search ,[object Object]
A Curator may also be the Publisher
Or a Curator may add metadata to someone else’s resources
 
 
A Model for OER Search (2) ,[object Object]
Curators & Feeds
Two Prototypes ,[object Object]
Nutch
Initial effort: Google CSE ,[object Object]
Optionally include annotations – facets and labels ,[object Object]
Output XML suitable for Google CSE
Scaling with CSE ,[object Object]
Labels and Facets worked best with fixed, limited vocabulary
License-filtered search unavailable
Nutch-based Prototype ,[object Object]

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Commodity Semantic Search: A Case Study of DiscoverEd

Notas del editor

  1. Good afternoon. My name is Nathan Yergler, and I&apos;m Chief Technology Officer at Creative Commons. This afternoon I&apos;m going to talk about a semantic enhanced search engine for education we&apos;ve been working on called DiscoverEd. It&apos;s built on commodity hardware and open source tools, and the software can be used for other domains. I&apos;m going to talk about some approaches we tried and rejected, and give you some information on tools you can use for building your own semantic search without investing in your own server farm.