Knowledge representation learning(KRL)aims to encode entities and relationships in various knowledge graphs into low-dimensional continuous vectors.It is popularly used in knowledge graph completion(or link prediction...Knowledge representation learning(KRL)aims to encode entities and relationships in various knowledge graphs into low-dimensional continuous vectors.It is popularly used in knowledge graph completion(or link prediction)tasks.Translation-based knowledge representation learning methods perform well in knowledge graph completion(KGC).However,the translation principles adopted by these methods are too strict and cannot model complex entities and relationships(i.e.,N-1,1-N,and N-N)well.Besides,these traditional translation principles are primarily used in static knowledge graphs and overlook the temporal properties of triplet facts.Therefore,we propose a temporal knowledge graph embedding model based on variable translation(TKGE-VT).The model proposes a new variable translation principle,which enables flexible transformation between entities and relationship embedding.Meanwhile,this paper considers the temporal properties of both entities and relationships and applies the proposed principle of variable translation to temporal knowledge graphs.We conduct link prediction and triplet classification experiments on four benchmark datasets:WN11,WN18,FB13,and FB15K.Our model outperforms baseline models on multiple evaluation metrics according to the experimental results.展开更多
基金supported partly by National Natural Science Foundation of China(Nos.62372119 and 62166003)the Project of Guangxi Science and Technology(Nos.GuiKeAB23026040 and GuiKeAD20159041)+3 种基金the Innovation Project of Guangxi Graduate Education(No.YCSW2023188)Key Lab of Education Blockchain and Intelligent Technology,Ministry of Education,Guangxi Normal University,Guilin,China,Intelligent Processing and the Research Fund of Guangxi Key Lab of Multi-source Information Mining&Security(Nos.20-A-01-01 and MIMS21-M01)Open Research Fund of Guangxi Key Lab of Humanmachine Interaction and Intelligent Decision(No.GXHIID2206)the Guangxi Collaborative Innovation Center of Multi-Source Information Integration and the Guangxi“Bagui”Teams for Innovation and Research,China.
文摘Knowledge representation learning(KRL)aims to encode entities and relationships in various knowledge graphs into low-dimensional continuous vectors.It is popularly used in knowledge graph completion(or link prediction)tasks.Translation-based knowledge representation learning methods perform well in knowledge graph completion(KGC).However,the translation principles adopted by these methods are too strict and cannot model complex entities and relationships(i.e.,N-1,1-N,and N-N)well.Besides,these traditional translation principles are primarily used in static knowledge graphs and overlook the temporal properties of triplet facts.Therefore,we propose a temporal knowledge graph embedding model based on variable translation(TKGE-VT).The model proposes a new variable translation principle,which enables flexible transformation between entities and relationship embedding.Meanwhile,this paper considers the temporal properties of both entities and relationships and applies the proposed principle of variable translation to temporal knowledge graphs.We conduct link prediction and triplet classification experiments on four benchmark datasets:WN11,WN18,FB13,and FB15K.Our model outperforms baseline models on multiple evaluation metrics according to the experimental results.
