Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to Borrow -- Relation Representation for Without-Mention
  Entity-Pairs for Knowledge Graph Completion   Prior work on integrating text corpora with knowledge graphs (KGs) to improve
Knowledge Graph Embedding (KGE) have obtained good performance for entities
that co-occur in sentences in text corpora. Such sentences (textual mentions of
entity-pairs) are represented as Lexicalised Dependency Paths (LDPs) between
two entities. However, it is not possible to represent relations between
entities that do not co-occur in a single sentence using LDPs. In this paper,
we propose and evaluate several methods to address this problem, where we
borrow LDPs from the entity pairs that co-occur in sentences in the corpus
(i.e. with mention entity pairs) to represent entity pairs that do not co-occur
in any sentence in the corpus (i.e. without mention entity pairs). We propose a
supervised borrowing method, SuperBorrow, that learns to score the suitability
of an LDP to represent a without-mention entity pair using pre-trained entity
embeddings and contextualised LDP representations. Experimental results show
that SuperBorrow improves the link prediction performance of multiple
widely-used prior KGE methods such as TransE, DistMult, ComplEx and RotatE.

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The categories of this paper are: nlin
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The categories of this paper are: plasm-ph
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The categories of this paper are: eess
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The categories of this paper are: hep-ex
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The categories of this paper are: alg-geom
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The categories of this paper are: physics
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The categories of this paper are: econ
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The categories of this paper are: q-alg
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The categories of this paper are: astro-ph
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The categories of this paper are: supr-con
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The categories of this paper are: funct-an
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The categories of this paper are: q-bio
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The categories of this paper are: quant-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: adap-org
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The categories of this paper are: chao-dyn