Journal article
Exploratory analysis of a crowdsourcing metadata tool for building terminological consensus in civil engineering
Automation in construction, v 166, 105627
01 Oct 2024
Abstract
The longstanding absence of common terminology across the Architecture, Engineering, and Construction (AEC) industry results in communication barriers and hinders smooth collaboration among professionals across these disciplines. To address this challenge, the potential of a crowdsourced methodology was investigated in this paper to assist in improving terminological consensus using an online platform: Yet Another Metadata Zoo (YAMZ). Participants from the academic form-finding community were engaged to interact using YAMZ. Definitions, comments, and votes were collected and analyzed to understand their quantitative and qualitative relationships. The results indicate that a crowdsourcing methodology can be employed in research groups to build terminological consensus and may enhance research through improved terminology production. Addressing each of these challenges could help reduce semantic ambiguity among stakeholders in AEC projects. It was concluded that a crowdsourced approach may offer a pathway for faster standards development, although a broader study involving stakeholders from the AEC field is necessary.
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Details
- Title
- Exploratory analysis of a crowdsourcing metadata tool for building terminological consensus in civil engineering
- Creators
- Isabel M. de Oliveira - Both authors contributed equally to this work and are considered joint first authorsScott McClellan - Drexel UniversityChristopher Rauch - Drexel UniversitySigrid Adriaenssens - Princeton UniversityJane Greenberg - Drexel University
- Publication Details
- Automation in construction, v 166, 105627
- Publisher
- Elsevier
- Grant note
- OAC-2118201 / Institute for Data-Driven Dynamical Design National Science Foundation (http://data.elsevier.com/vocabulary/SciValFunders/100000001)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:001276951400001
- Scopus ID
- 2-s2.0-85199159611
- Other Identifier
- 991022202110904721