Distance based knowledge graph embedding methods show promising results on link prediction task, on which two topics have been widely studied: one is the ability to handle complex relations, such as N-to-1, 1-to-N and N-to-N, the other is to encode various relation patterns, such as symmetry/antisymmetry. However, the existing methods fail to solve these two problems at the same time, which leads to unsatisfactory results. To mitigate this problem, we propose PairRE, a model with improved expressiveness and low computational requirement. PairRE represents each relation with paired vectors , where these paired vectors project connected two entities to relation specific locations. Beyond its ability to solve the aforementioned two problems , PairRE is advantageous to represent subrelation as it can capture both the similarities and differences of subrelations effectively . Given simple constraints on relation representations, PairRE can be the first model that is capable of encoding symmetry/antisymmetry, inverse, composition and subrelation relations . Experiments on link prediction benchmarks show PairREcan achieve either state-of-the-art or highly competitive performances. In addition, PairRE has shown encouraging results for encoding subrelation .
Distance based knowledge graph embedding methods show promising results on link prediction task, on which two topics have been widely studied: one is the ability to handle complex relations, such as N-to-1, 1-to-N and N-to-N, the other is to encode various relation patterns, such as symmetry/antisymmetry. However, the existing methods fail to solve these two problems at the same time, which leads to unsatisfactory results. To mitigate this problem, we propose PairRE, a model with paired vectors for each relation representation. The paired vectors enable an adaptive adjustment of the margin in loss function to fit for complex relations. Besides , PairRE is capable of encoding three important relation patterns, symmetry/antisymmetry, inverse and composition . Given simple constraints on relation representations, PairRE can encode subrelation further . Experiments on link prediction benchmarks demonstrate the proposed key capabilities of PairRE. Moreover, We set a new state-of-the-art on two knowledge graph datasets of the challenging Open Graph Benchmark .
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improved expressiveness and low computational requirement. PairRE represents each relation with paired vectors , where these paired vectors project connected two entities to relation specific locations. Beyond its ability to solve the aforementioned two problems
paired vectors for each relation representation. The paired vectors enable an adaptive adjustment of the margin in loss function to fit for complex relations. Besides
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advantageous to represent subrelation as it can capture both the similarities and differences of subrelations effectively
capable of encoding three important relation patterns, symmetry/antisymmetry, inverse and composition
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be the first model that is capable of encoding symmetry/antisymmetry, inverse, composition and subrelation relations
encode subrelation further
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show PairREcan achieve either
demonstrate the proposed key capabilities of PairRE. Moreover, We set a new
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or highly competitive performances. In addition, PairRE has shown encouraging results for encoding subrelation
on two knowledge graph datasets of the challenging Open Graph Benchmark