  Searching for concepts in science and technology is often a difficult task.
To facilitate concept search, different types of human-generated metadata have
been created to define the content of scientific and technical disclosures.
Classification schemes such as the International Patent Classification (IPC)
and MEDLINE's MeSH are structured and controlled, but require trained experts
and central management to restrict ambiguity (Mork, 2013). While unstructured
tags of folksonomies can be processed to produce a degree of structure
(Kalendar, 2010; Karampinas, 2012; Sarasua, 2012; Bragg, 2013) the freedom
enjoyed by the crowd typically results in less precision (Stock 2007).
  Existing classification schemes suffer from inflexibility and ambiguity.
Since humans understand language, inference, implication, abstraction and hence
concepts better than computers, we propose to harness the collective wisdom of
the crowd. To do so, we propose a novel classification scheme that is
sufficiently intuitive for the crowd to use, yet powerful enough to facilitate
search by analogy, and flexible enough to deal with ambiguity. The system will
enhance existing classification information. Linking up with the semantic web
and computer intelligence, a Citizen Science effort (Good, 2013) would support
innovation by improving the quality of granted patents, reducing duplicitous
research, and stimulating problem-oriented solution design.
  A prototype of our design is in preparation. A crowd-sourced fuzzy and
faceted classification scheme will allow for better concept search and improved
access to prior art in science and technology.
