8 papers
ProbSPARQL: Querying Knowledge Graphs with Multi-dimensional, Uncertain Numeric Data
Jingcheng Wu, Ratan Bahadur Thapa, Daniel Hernandez +2
The SFB 1574 Circular Factory is building a shared knowledge graph infrastructure for integrating data about returned products. A central challenge is that circular-factory data in…
GenAU: Language-Grounded Industrial Anomaly Understanding with Vision-Language Models
Hongkuan Zhou, Tristan Rehm, Nadeem Nazer +3
Industrial inspection requires more than binary anomaly detection: a practical system should determine whether an anomaly exists, localize the defective region, identify the defect…
Leveraging Graph Structure in Seq2Seq Models for Knowledge Graph Link Prediction
Luu Huu Phuc, Ratan Bahadur Thapa, Mojtaba Nayyeri +3
We introduce Graph-Augmented Sequence-to-Sequence (GA-S2S), a novel framework that integrates a T5-small encoder-decoder with a Relational Graph Attention Network (RGAT) to improve…
Scalable Uncertainty Reasoning in Knowledge Graphs
Jingcheng Wu
Knowledge Graphs are pivotal for semantic data integration. The real-world data they model is often inherently uncertain. Within knowledge graphs, uncertainty manifests in three di…
Towards Foundation Models for Relational Databases with Language Models and Graph Neural Networks
Jingcheng Wu, Ratan Bahadur Thapa, Mojtaba Nayyeri +4
Relational databases store much of the world's structured information, and they are essential for driving complex predictive applications. However, deep learning progress on relati…
Seeing and Knowing in the Wild: Open-domain Visual Entity Recognition with Large-scale Knowledge Graphs via Contrastive Learning
Hongkuan Zhou, Lavdim Halilaj, Sebastian Monka +5
Open-domain visual entity recognition aims to identify and link entities depicted in images to a vast and evolving set of real-world concepts, such as those found in Wikidata. Unli…
Certainty in Uncertainty: Reasoning over Uncertain Knowledge Graphs with Statistical Guarantees
Yuqicheng Zhu, Jingcheng Wu, Yizhen Wang +4
Uncertain knowledge graph embedding (UnKGE) methods learn vector representations that capture both structural and uncertainty information to predict scores of unseen triples. Howev…
DyGMamba: Efficiently Modeling Long-Term Temporal Dependency on Continuous-Time Dynamic Graphs with State Space Models
Zifeng Ding, Yifeng Li, Yuan He +5
Learning useful representations for continuous-time dynamic graphs (CTDGs) is challenging, due to the concurrent need to span long node interaction histories and grasp nuanced temp…