Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Context-Enhanced Entity and Relation Embedding for Knowledge Graph
  Completion   Most researches for knowledge graph completion learn representations of
entities and relations to predict missing links in incomplete knowledge graphs.
However, these methods fail to take full advantage of both the contextual
information of entity and relation. Here, we extract contexts of entities and
relations from the triplets which they compose. We propose a model named AggrE,
which conducts efficient aggregations respectively on entity context and
relation context in multi-hops, and learns context-enhanced entity and relation
embeddings for knowledge graph completion. The experiment results show that
AggrE is competitive to existing models.

31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
27
The categories of this paper are: comp-gas
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
17
The categories of this paper are: q-fin
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The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn