Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Survey of Embedding Space Alignment Methods for Language and Knowledge
  Graphs   Neural embedding approaches have become a staple in the fields of computer
vision, natural language processing, and more recently, graph analytics. Given
the pervasive nature of these algorithms, the natural question becomes how to
exploit the embedding spaces to map, or align, embeddings of different data
sources. To this end, we survey the current research landscape on word,
sentence and knowledge graph embedding algorithms. We provide a classification
of the relevant alignment techniques and discuss benchmark datasets used in
this field of research. By gathering these diverse approaches into a singular
survey, we hope to further motivate research into alignment of embedding spaces
of varied data types and sources.

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The categories of this paper are: q-bio
22
The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: adap-org
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The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
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The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an