Exploratory analysis of a crowdsourcing metadata tool for building terminological consensus in civil engineering
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...
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Veröffentlicht in: | Automation in construction 2024-10, Vol.166, p.105627, Article 105627 |
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Sprache: | eng |
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Zusammenfassung: | 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.
•Crowdsourcing tool has promising applicability in the civil engineering community.•AEC industry’s terminological overlaps show intersections requiring disambiguation.•Semantic disambiguation aids interoperability digital workflows and communication.•User statistics show high levels of positive and negative engagement.•Statistics regarding definitions point toward broader study. |
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ISSN: | 0926-5805 |
DOI: | 10.1016/j.autcon.2024.105627 |