A neural regression model (LitEm) that enables transductive knowledge graph embedding models to predict numerical attributes within knowledge graphs and a co-training framework that jointly trains state-of-the-art transductive knowledge graph embedding models with LitEm, which improves link prediction performance mainly for bilinear models and simultaneously enables them to predict numerical attributes.
Rupesh Sapkota, Louis Mozart Kamdem Teyou, Moshood Yekini et al.· 0 citations
This work proposes MARS, a scalable knowledge graph question answering (KGQA) approach that requires no model fine-tuning, and performs a structured retrieval procedure that links question entities to the KG and iteratively retrieves relevant next-hop information.
Nikit Srivastava, Daniel Vollmers, René Speck et al.· 0 citations
This paper proposes Class Expression Simplifier (CES), a novel algorithm for the syntactic simplification of class expressions in Description Logics (DL), which aims to preserve formal semantics while reducing representational complexity.
Alkid Baci, N'Dah Jean Kouagou, Caglar Demir et al.· 0 citations