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

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Abstract
Even though it can provide design teams with valuable performance insights and enhance decision-making, 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 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 them 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 performance-oriented design. We demonstrate an initial implementation of a linked data-based system for generation of recommendations.
Keywords
BIM, data mining, machine learning, sensor data, semantics, decision support, recommender system

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MLA
Petrova, Ekaterina, et al. “Semantic Data Mining and Linked Data for a Recommender System in the AEC Industry.” Proceedings of the 2019 European Conference for Computing in Construction, European Council on Computing in Construction (EC3), 2019, pp. 172–81.
APA
Petrova, E., Pauwels, P., Svidt, K., & Jensen, R. L. (2019). Semantic data mining and linked data for a recommender system in the AEC industry. In Proceedings of the 2019 European Conference for Computing in Construction (pp. 172–181). Chania, Crete: European Council on Computing in Construction (EC3).
Chicago author-date
Petrova, Ekaterina, Pieter Pauwels, Kjeld Svidt, and Rasmus Lund Jensen. 2019. “Semantic Data Mining and Linked Data for a Recommender System in the AEC Industry.” In Proceedings of the 2019 European Conference for Computing in Construction, 172–81. European Council on Computing in Construction (EC3).
Chicago author-date (all authors)
Petrova, Ekaterina, Pieter Pauwels, Kjeld Svidt, and Rasmus Lund Jensen. 2019. “Semantic Data Mining and Linked Data for a Recommender System in the AEC Industry.” In Proceedings of the 2019 European Conference for Computing in Construction, 172–181. European Council on Computing in Construction (EC3).
Vancouver
1.
Petrova E, Pauwels P, Svidt K, Jensen RL. Semantic data mining and linked data for a recommender system in the AEC industry. In: Proceedings of the 2019 European Conference for Computing in Construction. European Council on Computing in Construction (EC3); 2019. p. 172–81.
IEEE
[1]
E. Petrova, P. Pauwels, K. Svidt, and R. L. Jensen, “Semantic data mining and linked data for a recommender system in the AEC industry,” in Proceedings of the 2019 European Conference for Computing in Construction, Chania, Crete, 2019, pp. 172–181.
@inproceedings{8633669,
  abstract     = {Even though it can provide design teams with valuable performance insights and enhance decision-making, 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 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 them 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 performance-oriented design. We demonstrate an initial implementation of a linked data-based system for generation of recommendations.},
  author       = {Petrova, Ekaterina and Pauwels, Pieter and Svidt, Kjeld and Jensen, Rasmus Lund},
  booktitle    = {Proceedings of the 2019 European Conference for Computing in Construction},
  isbn         = {9781910963371},
  issn         = {2684-1150},
  keywords     = {BIM,data mining,machine learning,sensor data,semantics,decision support,recommender system},
  language     = {eng},
  location     = {Chania, Crete},
  pages        = {172--181},
  publisher    = {European Council on Computing in Construction (EC3)},
  title        = {Semantic data mining and linked data for a recommender system in the AEC industry},
  url          = {http://dx.doi.org/10.35490/ec3.2019.192},
  year         = {2019},
}

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