RotatE：Knowledge Graph Embedding by Relational Rotation in Complex Space.pdf下载

We study the problem of learning representations of entities and relations in knowledgegraphsforpredictingmissinglinks. Thesuccessofsuchataskheavily relies on the ability of modeling and inferring the patterns of (or between) the relations. Inthispaper,wepresentanewapproachforknowledgegraphembedding called RotatE, which is able to model and infer various relation patterns including: symmetry/antisymmetry,inversion,andcomposition. Specifically,theRotatE modeldefineseachrelationasarotationfromthesourceentitytothetargetentity inthecomplexvectorspace. Inaddition,weproposeanovelself-adversarialnegativesamplingtechniqueforefficientlyandeffectivelytrainingtheRotatEmodel. Experimentalresultsonmultiplebenchmarkknowledgegraphsshowthattheproposed RotatE model is not only scalable, but also able to infer and model various relation patterns and significantly outperform existing state-of-the-art models for link prediction.