Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Systematic Investigation of KB-Text Embedding Alignment at Scale   Knowledge bases (KBs) and text often contain complementary knowledge: KBs
store structured knowledge that can support long range reasoning, while text
stores more comprehensive and timely knowledge in an unstructured way.
Separately embedding the individual knowledge sources into vector spaces has
demonstrated tremendous successes in encoding the respective knowledge, but how
to jointly embed and reason with both knowledge sources to fully leverage the
complementary information is still largely an open problem. We conduct a
large-scale, systematic investigation of aligning KB and text embeddings for
joint reasoning. We set up a novel evaluation framework with two evaluation
tasks, few-shot link prediction and analogical reasoning, and evaluate an array
of KB-text embedding alignment methods. We also demonstrate how such alignment
can infuse textual information into KB embeddings for more accurate link
prediction on emerging entities and events, using COVID-19 as a case study.

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The categories of this paper are: acc-phys
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The categories of this paper are: q-fin
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The categories of this paper are: q-bio
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The categories of this paper are: nucl-th
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The categories of this paper are: physics
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The categories of this paper are: dg-ga
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The categories of this paper are: ao-sci
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The categories of this paper are: supr-con
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The categories of this paper are: chem-ph
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The categories of this paper are: funct-an
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The categories of this paper are: gr-qc
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The categories of this paper are: patt-sol
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The categories of this paper are: mtrl-th
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The categories of this paper are: stat
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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-th