Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: KGE-CL: Contrastive Learning of Knowledge Graph Embeddings   Learning the embeddings of knowledge graphs is vital in artificial
intelligence, and can benefit various downstream applications, such as
recommendation and question answering. In recent years, many research efforts
have been proposed for knowledge graph embedding. However, most previous
knowledge graph embedding methods ignore the semantic similarity between the
related entities and entity-relation couples in different triples since they
separately optimize each triple with the scoring function. To address this
problem, we propose a simple yet efficient contrastive learning framework for
knowledge graph embeddings, which can shorten the semantic distance of the
related entities and entity-relation couples in different triples and thus
improve the expressiveness of knowledge graph embeddings. We evaluate our
proposed method on three standard knowledge graph benchmarks. It is noteworthy
that our method can yield some new state-of-the-art results, achieving 51.2%
MRR, 46.8% Hits@1 on the WN18RR dataset, and 59.1% MRR, 51.8% Hits@1 on the
YAGO3-10 dataset.

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The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
10
The categories of this paper are: q-bio
2
The categories of this paper are: math
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
19
The categories of this paper are: eess
16
The categories of this paper are: stat