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Neural Regression with Embeddings for Numerical Attribute Prediction in Knowledge Graphs
Rupesh Sapkota, Louis Mozart Kamdem Teyou, Moshood Yekini +2
In recent years, transductive knowledge graph embedding models have been applied to tasks such as link prediction and query answering. Although knowledge graphs often contain rich…
Parameter Averaging in Link Prediction
Rupesh Sapkota, Caglar Demir, Arnab Sharma +1
Ensemble methods are widely employed to improve generalization in machine learning. This has also prompted the adoption of ensemble learning for the knowledge graph embedding (KGE)…
Resilience in Knowledge Graph Embeddings
Arnab Sharma, N'Dah Jean Kouagou, Axel-Cyrille Ngonga Ngomo
In recent years, knowledge graphs have gained interest and witnessed widespread applications in various domains, such as information retrieval, question-answering, recommendation s…
Inference over Unseen Entities, Relations and Literals on Knowledge Graphs
Caglar Demir, N'Dah Jean Kouagou, Arnab Sharma +1
In recent years, knowledge graph embedding models have been successfully applied in the transductive setting to tackle various challenging tasks including link prediction, and quer…
Performance Evaluation of Knowledge Graph Embedding Approaches under Non-adversarial Attacks
Sourabh Kapoor, Arnab Sharma, Michael Röder +2
Knowledge Graph Embedding (KGE) transforms a discrete Knowledge Graph (KG) into a continuous vector space facilitating its use in various AI-driven applications like Semantic Searc…
Adaptive Stochastic Weight Averaging
Caglar Demir, Arnab Sharma, Axel-Cyrille Ngonga Ngomo
Ensemble models often improve generalization performances in challenging tasks. Yet, traditional techniques based on prediction averaging incur three well-known disadvantages: the…