Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Inductive Entity Representations from Text via Link Prediction   Knowledge Graphs (KG) are of vital importance for multiple applications on
the web, including information retrieval, recommender systems, and metadata
annotation. Regardless of whether they are built manually by domain experts or
with automatic pipelines, KGs are often incomplete. Recent work has begun to
explore the use of textual descriptions available in knowledge graphs to learn
vector representations of entities in order to preform link prediction.
However, the extent to which these representations learned for link prediction
generalize to other tasks is unclear. This is important given the cost of
learning such representations. Ideally, we would prefer representations that do
not need to be trained again when transferring to a different task, while
retaining reasonable performance.
  In this work, we propose a holistic evaluation protocol for entity
representations learned via a link prediction objective. We consider the
inductive link prediction and entity classification tasks, which involve
entities not seen during training. We also consider an information retrieval
task for entity-oriented search. We evaluate an architecture based on a
pretrained language model, that exhibits strong generalization to entities not
observed during training, and outperforms related state-of-the-art methods (22%
MRR improvement in link prediction on average). We further provide evidence
that the learned representations transfer well to other tasks without
fine-tuning. In the entity classification task we obtain an average improvement
of 16% in accuracy compared with baselines that also employ pre-trained models.
In the information retrieval task, we obtain significant improvements of up to
8.8% in NDCG@10 for natural language queries. We thus show that the learned
representations are not limited KG-specific tasks, and have greater
generalization properties than evaluated in previous work.

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The categories of this paper are: eess
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The categories of this paper are: alg-geom
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The categories of this paper are: solv-int
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The categories of this paper are: supr-con
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The categories of this paper are: plasm-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-ex
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The categories of this paper are: adap-org
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The categories of this paper are: q-bio
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The categories of this paper are: cond-mat
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The categories of this paper are: funct-an
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The categories of this paper are: nlin
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The categories of this paper are: hep-th
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The categories of this paper are: q-alg
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The categories of this paper are: chao-dyn
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The categories of this paper are: gr-qc