Crowd-Sourcing Fuzzy and Faceted Classification for Concept Search
Richard Absalom, Marcus Luczak-Rosch, Dap Hartmann, and Aske Plaat

TL;DR
This paper proposes a crowd-sourced fuzzy and faceted classification system to improve concept search in scientific and technological domains, addressing the limitations of traditional structured schemes.
Contribution
It introduces a novel, intuitive classification scheme leveraging crowd wisdom to enhance search capabilities and handle ambiguity in scientific content.
Findings
Concept search is improved through crowd-sourced classification.
The scheme enhances existing classification systems with flexibility and ambiguity handling.
Prototype development is underway to validate the approach.
Abstract
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…
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Taxonomy
TopicsMobile Crowdsensing and Crowdsourcing · Misinformation and Its Impacts · Advanced Text Analysis Techniques
