Artificial Intelligence

    

Triplere: Knowledge Graph Embeddings Via Triple Relation Vectors

Authors: Long Yu, ZhiCong Luo, Deng Lin, HongZhu Li, HuanYong Liu, YaFeng Deng

Knowledge representation is a classic problem in Knowledge graphs. Distance-based models have made great progress. The most significant recent developments in this direction have been those of Rotate[1] and PairRE[2], which focuses on expressing relationships as projections of nodes. However TransX series Model(TransE[3], TransH[4], TransR[5]) expresses relationships as translations of nodes. To date, the problem of the Combination of Projection and translation has received scant attention in the research literature. Hence, we propose TripleRE, a method that models relationships by projections and translations. Compared with the other knowledge representation model, we achieve the best results on the ogbl-wikikg2 dataset.

Comments: 6 Pages.

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Submission history

[v1] 2021-12-17 20:54:35
[v2] 2021-12-25 21:44:48
[v3] 2022-02-24 21:03:49

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