arXiv:2606. 29860v1 Announce Type: new Abstract: Knowledge graphs (KGs) organize real-world knowledge as triplets and underpin many downstream applications.
By Zihao Zheng, Borui Cai, Yao Zhao, Keshav Sood, Yong Xiang
arXiv:2606. 16509v1 Announce Type: new Abstract: Link prediction in knowledge graphs fundamentally depends on the quality of learned embeddings for entities and relations.
By Mohommad Esmaei Khani, Mahdieh Hasheminejad, Ali Taherkhani, Hossein Hajiabolhassan
arXiv:2609. 03487v1 Announce Type: cross Abstract: Knowledge graph embedding (KGE) demonstrates its effectiveness for predicting missing links in knowledge graphs (KGs) by projecting entities and relations into a low-dimensional vector space.
By Junsik Kim, Kangil Kim
arXiv:2606. 27967v1 Announce Type: new Abstract: Real-world knowledge graphs are often incomplete, lacking many valid facts.
By Yike Liu, Peijia Xie, Chao He, Huiling Zhu
The paper introduces ImbalancE, an inference‑time latent search method that mitigates degree imbalance bias in Knowledge Graph Embedding models. It targets the problematic prediction of target entities with much lower degrees than anchor entities, a common issue in recommender systems and other applications. Experiments on benchmark datasets show that ImbalancE improves predictions on the most imbalanced triples compared to conventional methods.
By Alberto Bernardi, Luca Costabello, Christophe Gueret
arXiv:2607. 03154v1 Announce Type: cross Abstract: Multi-domain knowledge graph completion (MKGC) aims to improve missing triple prediction in a target KG by transferring knowledge from other support KGs.
By Jiawei Sheng, Taoyu Su, Xixun Lin, Xiaodong Li, Tingwen Liu