Semantic data mining and linked data for a recommender system in the AEC industry

Ekaterina Petrova1, Pieter Pauwels2, Kjeld Svidt1, Rasmus Jensen1
1 Aalborg University, Denmark
2 Ghent University, Belgium


DOI: 10.35490/EC3.2019.192
Abstract: Even though it can provide design teams with valuable performance insights and enhance their decision-making processes, monitored building data is rarely reused in an effective feedback loop from operation to design. Data mining allows users to obtain such insights from the large datasets generated throughout the building life cycle. Furthermore, semantic web and linked data technologies allow to formally represent the built environment and retrieve knowledge in response to domain-specific requirements. Both approaches have independently established themselves as powerful aids in decision-making. Combining both can enrich data mining processes with domain knowledge and facilitate knowledge discovery, representation and reuse. In this article, we look into the available data mining techniques and investigate to what extent they can be fused with semantic web technologies, to provide recommendations to the end user in a performance-oriented design process. We demonstrate an initial implementation of a linked data-based system for generation of recommendations.
Keywords: BIM, Knowledge Discovery, Semantic Data Modelling, Recommender Systems
Pages: 172 - 181
Paper:
http://ec-3.org/conf2019/contribution_192_final/

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