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Crowd-Sourcing Fuzzy and Faceted Classification for Concept Search by Absalom, Richard; Luczak-Rosch, Marcus; Hartmann, Dap; Plaat, Aske is a scholarly article available to read on EtoBox.

What is Crowd-Sourcing Fuzzy and Faceted Classification for Concept Search about?

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 wit

Author

Absalom, Richard; Luczak-Rosch, Marcus; Hartmann, Dap; Plaat, Aske

Published

2014

Language

EN