Papers by Steffen Staab
Certainty in Uncertainty: Reasoning over Uncertain Knowledge Graphs with Statistical Guarantees (2025.emnlp-main)
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Yuqicheng Zhu, Jingcheng Wu, Yizhen Wang, Hongkuan Zhou, Jiaoyan Chen, Evgeny Kharlamov, Steffen Staab
| Challenge: | Existing methods produce only point estimates, without quantifying predictive uncertainty—limiting their reliability in high-stakes applications where understanding confidence in predictions is crucial. |
| Approach: | They propose a framework that generates prediction intervals guaranteed to contain the true score with a user-specified level of confidence. |
| Outcome: | The proposed framework generates prediction intervals guaranteed to contain the true score with a user-specified level of confidence. |
Knowledge Graph Embeddings using Neural Ito Process: From Multiple Walks to Stochastic Trajectories (2023.findings-acl)
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Mojtaba Nayyeri, Bo Xiong, Majid Mohammadi, Mst. Mahfuja Akter, Mirza Mohtashim Alam, Jens Lehmann, Steffen Staab
| Challenge: | Existing knowledge graph embeddings have problems expressing knowledge graphs because they model a specific relation r from a head h to tails by transitioning deterministically to exactly one other point in the embeddable space. |
| Approach: | They propose a framework that models relations between nodes by relation-specific, stochastic transitions. |
| Outcome: | The proposed framework is expressive and generic subsuming state-of-the-art models operating on low-dimensional manifolds. |
Conformalized Answer Set Prediction for Knowledge Graph Embedding (2025.naacl-long)
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| Challenge: | Knowledge graph embeddings (KGE) map entities and predicates into numerical vectors, providing non-classical reasoning capabilities based on similarities and analogies between entities and relations. |
| Approach: | They propose to use knowledge graph embeddings to provide non-classical reasoning capabilities by exploiting similarities and analogies between entities and relations. |
| Outcome: | The proposed model can generate answer sets with probabilistic guarantees on four benchmark datasets and is scaled well with respect to the difficulty of the query. |
What Breaks Knowledge Graph based RAG? Benchmarking and Empirical Insights into Reasoning under Incomplete Knowledge (2026.eacl-long)
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Dongzhuoran Zhou, Yuqicheng Zhu, Xiaxia Wang, Hongkuan Zhou, Yuan He, Jiaoyan Chen, Steffen Staab, Evgeny Kharlamov
| Challenge: | Existing evaluation metrics and lenient answer matching criteria obscure meaningful comparisons. |
| Approach: | They propose a general method for constructing benchmarks and a method to assess KG-RAG methods under incomplete knowledge. |
| Outcome: | The proposed method systematically assesses KG-RAG methods under incomplete knowledge. |
Is Language Modeling Enough? Evaluating Effective Embedding Combinations (2020.lrec-1)
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Rudolf Schneider, Tom Oberhauser, Paul Grundmann, Felix Alexander Gers, Alexander Loeser, Steffen Staab
| Challenge: | specialized embeddings are not available for tasks like entity linking or paragraph classification. |
| Approach: | They evaluate whether universal embeddings can be complemented by specialized embeddables. |
| Outcome: | The proposed embeddings outperform state-of-the-art embeddables without any fine-tuning. |
SEMMA: A Semantic Aware Knowledge Graph Foundation Model (2025.emnlp-main)
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Arvindh Arun, Sumit Kumar, Mojtaba Nayyeri, Bo Xiong, Ponnurangam Kumaraguru, Antonio Vergari, Steffen Staab
| Challenge: | Existing Knowledge Graph Foundation Models (KGFMs) rely on graph structure, overlooking the rich semantic signals encoded in textual attributes. |
| Approach: | They propose a dual-module KGFM that integrates transferable textual semantics alongside structure to generate relation identifiers. |
| Outcome: | The proposed model outperforms ULTRA and ULtra in fully inductive link prediction in more challenging generalization settings. |
Predictive Multiplicity of Knowledge Graph Embeddings in Link Prediction (2024.findings-emnlp)
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| Challenge: | Knowledge graph embeddings (KGE) models are often used to predict missing links for knowledge graphs (KGs) however, multiple KG embedds can give conflicting predictions for unseen queries. |
| Approach: | They define predictive multiplicity in link prediction and introduce evaluation metrics to measure it using commonly used benchmark datasets. |
| Outcome: | The proposed methods significantly mitigat conflicts by 66% to 78% in link prediction. |
SCAIR: Schema-Conditioned Agentic Iterative Reasoning for Enterprise Knowledge Graphs (2026.acl-industry)
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Prateek Chaturvedi, Yuqicheng Zhu, Hongkuan Zhou, Dongzhuoran Zhou, Yunjie He, Steffen Staab, Fei Du, Jie Tang, Evgeny Kharlamov
| Challenge: | Existing agentic approaches for Knowledge Graph-based Retrieval-Augmented Generation fail to generalize to real-world enterprise Knowledge graphs (KGs) dense, schema-driven, and operationally constrained, requiring a training-free framework. |
| Approach: | They propose a training-free framework that integrates structured planning with controlled iterative reasoning by injecting schema-conditioned structural priors and enforcing schemas during multi-hop reasoning. |
| Outcome: | The proposed framework significantly improves on a real-world enterprise-oriented benchmark constructed from a Configuration Management DataBase (CMDB). |
Predicate-Conditional Conformalized Answer Sets for Knowledge Graph Embeddings (2025.findings-acl)
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| Challenge: | Existing methods provide probabilistic guarantees over a reference set of queries and answers, but they fail to identify when the answers to a query are uncertain. |
| Approach: | They propose a method that approximates predicate-conditional coverage guarantees while maintaining compact prediction sets. |
| Outcome: | The proposed method provides predicate-conditional coverage guarantees while maintaining compact prediction sets. |
Shrinking Embeddings for Hyper-Relational Knowledge Graphs (2023.acl-long)
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| Challenge: | Existing studies have focused on binary relational KGs where each fact is represented by a triple. |
| Approach: | They propose a geometric hyper-relational KG embedding method that explicitly models qualifier monotonicity, qualifier implication, and qualifier mutual exclusion. |
| Outcome: | The proposed method outperforms existing methods on three benchmarks of hyper-relational KGs. |
Disambiguating Emotional Connotations of Words Using Contextualized Word Representations (2024.starsem-1)
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| Challenge: | BERT, RoBERTa, XLNet, and GPT-2 models effectively discern emotional connotations of words, demonstrating superior performance and greater resilience against biases. |
| Approach: | They propose to use contextualized word representations to examine how words can be used to distinguish emotional connotations across contexts. |
| Outcome: | The proposed models show that they can distinguish emotional connotations of words in different contexts. |