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So, what's all the "fuzz" about? 
Here's a brief explanation of the paper contributions 
https://twitter.com/j_gomero/status/1359800008418156544
Here's a brief explanation of the paper contributions https://twitter.com/j_gomero/status/1359800008418156544
(Before starting, thanks again to @CSEI_imperial for supporting the research work, and @FerBobillo, @MiguelMolinaSo, Nacho Huitzil for pushing the paper forward.)
The Building Information Model (BIM) is a digital representation of a building used to document its lifecycle, from design to operation. BIMs are encoded with the IFC standard, which is supported by building modelling software such as Revit.
IFC has some limitations compared to modern data modelling languages --if we are still considering the Semantic Web as modern technology 
. I.e., there are many proposals to make IFC "more semantic"; e.g. P. Pauwels et al's. https://doi.org/10.1016/j.autcon.2016.10.003
. I.e., there are many proposals to make IFC "more semantic"; e.g. P. Pauwels et al's. https://doi.org/10.1016/j.autcon.2016.10.003
Wouldn't it be nice to retrieve 'large windows with north orientation' from the BIM? Sure it would! For that reason, we proposed extending semantic BIMs with imprecise operators to support this kind of queries. But this had a problem: computation time. https://doi.org/10.1016/j.autcon.2015.04.007
Hence, in this long-overdue paper, we propose a new algorithm to solve "fuzzy BIM queries". Details on fuzzy ontologies may seem a bit obscure, but the paper shows that the algorithm works well even for large models and inexpensive computers. And the software is open source 
Extra ball: This approach can be applied to many other domains involving large knowledge graphs. In case you're wondering: yes, that includes social network analysis for tracking disinformation! 
