artwork by aaron koblIn
as Wikipedia often merge user in-
puts tightly, and require users to edit
and merge one another’s inputs. A
well-known artifact is software (such
as Apache, Linux, Hadoop). Another
popular artifact is textual knowledge
bases (KBs). To build such KBs (such as
Wikipedia), users contribute data such
as sentences, paragraphs, Web pages,
then edit and merge one another’s
contributions. The knowledge capture
( k-cap.org) and AI communities have
studied building such KBs for over a
decade. A well-known early attempt is
openmind,
28 which enlists volunteers
to build a KB of commonsense facts
(for example, “the sky is blue”). Re-
cently, the success of Wikipedia has in-
spired many “community wikipedias,”
such as Intellipedia (for the U.S. intel-
ligence community) and EcoliHub (at
ecolicommunity.org, to capture all in-
formation about the E. coli bacterium).
the sheep market by aaron Koblin is a
collection of 10,000 sheep made by workers
on amazon’s mechanical turk. Workers were
paid $0.02 (usD) to “draw a sheep facing to
the left.” animations of each sheep’s creation
may be viewed at thesheepmarket.com.
ers can create and populate schemas
to describe topics of interest, and build
collections of interlinked topics using
a flexible graph model of data. As yet
another example, Google Fusion Tables ( tables.googlelabs.com) lets users
upload tabular data and collaborate
on it by merging tables from different
sources, commenting on data items,
and sharing visualizations on the Web.
Several recent academic projects
have also studied building structured
