Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Build2Vec: Building Representation in Vector Space   In this paper, we represent a methodology of a graph embeddings algorithm
that is used to transform labeled property graphs obtained from a Building
Information Model (BIM). Industrial Foundation Classes (IFC) is a standard
schema for BIM, which is utilized to convert the building data into a graph
representation. We used node2Vec with biased random walks to extract semantic
similarities between different building components and represent them in a
multi-dimensional vector space. A case study implementation is conducted on a
net-zero-energy building located at the National University of Singapore
(SDE4). This approach shows promising machine learning applications in
capturing the semantic relations and similarities of different building
objects, more specifically, spatial and spatio-temporal data.

29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
16
The categories of this paper are: stat
25
The categories of this paper are: patt-sol
7
The categories of this paper are: hep-th
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
2
The categories of this paper are: math