Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Reasoning Over Paths via Knowledge Base Completion   Reasoning over paths in large scale knowledge graphs is an important problem
for many applications. In this paper we discuss a simple approach to
automatically build and rank paths between a source and target entity pair with
learned embeddings using a knowledge base completion model (KBC). We assembled
a knowledge graph by mining the available biomedical scientific literature and
extracted a set of high frequency paths to use for validation. We demonstrate
that our method is able to effectively rank a list of known paths between a
pair of entities and also come up with plausible paths that are not present in
the knowledge graph. For a given entity pair we are able to reconstruct the
highest ranking path 60% of the time within the the top 10 ranked paths and
achieve 49% mean average precision. Our approach is compositional since any KBC
model that can produce vector representations of entities can be used.

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The categories of this paper are: hep-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: math-ph
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The categories of this paper are: hep-th
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The categories of this paper are: acc-phys
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The categories of this paper are: nlin
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The categories of this paper are: econ
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The categories of this paper are: adap-org
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The categories of this paper are: comp-gas
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The categories of this paper are: ao-sci
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The categories of this paper are: math
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The categories of this paper are: bayes-an
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The categories of this paper are: stat
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The categories of this paper are: eess
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The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-th