1 citations · 2 across the 10 of their papers we have counts for
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ArgRAG: Explainable Retrieval Augmented Generation using Quantitative Bipolar Argumentation
Yuqicheng Zhu, Nico Potyka, Daniel Hernández +6
Retrieval-Augmented Generation (RAG) enhances large language models by incorporating external knowledge, yet suffers from critical limitations in high-stakes domains -- namely, sen…
Predicate-Conditional Conformalized Answer Sets for Knowledge Graph Embeddings
Yuqicheng Zhu, Daniel Hernández, Yuan He +4
Uncertainty quantification in Knowledge Graph Embedding (KGE) methods is crucial for ensuring the reliability of downstream applications. A recent work applies conformal prediction…
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…
Conformalized Answer Set Prediction for Knowledge Graph Embedding
Yuqicheng Zhu, Nico Potyka, Jiarong Pan +4
Knowledge graph embeddings (KGE) apply machine learning methods on knowledge graphs (KGs) to provide non-classical reasoning capabilities based on similarities and analogies. The l…
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…
Approximating Probabilistic Inference in Statistical EL with Knowledge Graph Embeddings
Yuqicheng Zhu, Nico Potyka, Bo Xiong +4
Statistical information is ubiquitous but drawing valid conclusions from it is prohibitively hard. We explain how knowledge graph embeddings can be used to approximate probabilisti…