3 citations · 4 across the 2 of their papers we have counts for
10 papers
Generating Ontologies via Knowledge Graph Query Embedding Learning
Yunjie He, Daniel Hernandez, Mojtaba Nayyeri +4
Query embedding approaches answer complex logical queries over incomplete knowledge graphs (KGs) by computing and operating on low-dimensional vector representations of entities, r…
Predictive Multiplicity of Knowledge Graph Embeddings in Link Prediction
Yuqicheng Zhu, Nico Potyka, Mojtaba Nayyeri +4
Knowledge graph embedding (KGE) models are often used to predict missing links for knowledge graphs (KGs). However, multiple KG embeddings can perform almost equally well for link…
Alleviating Over-Smoothing via Aggregation over Compact Manifolds
Dongzhuoran Zhou, Hui Yang, Bo Xiong +2
Graph neural networks (GNNs) have achieved significant success in various applications. Most GNNs learn the node features with information aggregation of its neighbors and feature…
Pre-Training and Prompting for Few-Shot Node Classification on Text-Attributed Graphs
Huanjing Zhao, Beining Yang, Yukuo Cen +6
The text-attributed graph (TAG) is one kind of important real-world graph-structured data with each node associated with raw texts. For TAGs, traditional few-shot node classificati…
Literal-Aware Knowledge Graph Embedding for Welding Quality Monitoring: A Bosch Case
Zhipeng Tan, Baifan Zhou, Zhuoxun Zheng +5
Recently there has been a series of studies in knowledge graph embedding (KGE), which attempts to learn the embeddings of the entities and relations as numerical vectors and mathem…
Scaling Data Science Solutions with Semantics and Machine Learning: Bosch Case
Baifan Zhou, Nikolay Nikolov, Zhuoxun Zheng +5
Industry 4.0 and Internet of Things (IoT) technologies unlock unprecedented amount of data from factory production, posing big data challenges in volume and variety. In that contex…