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Scaling Multilingual Mixture-of-Experts: How Language Coverage Changes Capacity and Compute Allocation
Joel Dag, Nikit Srivastava, Hamada M. Zahera +1
Multilingual models share capacity across languages, so adding languages can hurt performance on existing ones. Mixture-of-experts models expand this capacity with little additiona…
Neural Regression with Embeddings for Numerical Attribute Prediction in Knowledge Graphs
Rupesh Sapkota, Louis Mozart Kamdem Teyou, Moshood Yekini +2
In recent years, transductive knowledge graph embedding models have been applied to tasks such as link prediction and query answering. Although knowledge graphs often contain rich…
Syntactic Simplification of OWL Class Expressions
Alkid Baci, N'Dah Jean Kouagou, Caglar Demir +1
Class expression learning often produces complex OWL class expressions that are difficult to interpret and reason over. However, by following theoretically grounded simplification…
MARS: Multi-hop Adaptive Retrieval and SPARQL Generation for KGQA
Nikit Srivastava, Daniel Vollmers, René Speck +3
Large language models (LLMs) have demonstrated strong reasoning performance, but their tendency to hallucinate limits their reliability in knowledge-intensive tasks requiring up-to…
Benchmarking Knowledge Editing using Logical Rules
Tatiana Moteu Ngoli, NDah Jean Kouagou, Hamada M. Zahera +1
Large Language Models (LLMs) are increasingly deployed in real-world applications that require access to up-to-date knowledge. However, retraining LLMs is computationally expensive…
Inconsistent Databases and Argumentation Frameworks with Collective Attacks
Yasir Mahmood, Jonni Virtema, Timon Barlag +1
The connection between subset-maximal repairs for inconsistent databases involving various integrity constraints and acceptable sets of arguments within argumentation frameworks ha…
Rethinking Explanations: Formalizing Contrast in Description Logics
Yasir Mahmood, Arnab Sharma, Axel-Cyrille Ngonga Ngomo +1
There has been a growing interest in explaining entailments over description logic (DL) knowledge bases. The existing explanation formalisms focus on justifications to explain true…
Learning Chain Of Thoughts Prompts for Predicting Entities, Relations, and even Literals on Knowledge Graphs
Alkid Baci, Luke Friedrichs, Caglar Demir +2
Knowledge graph embedding (KGE) models perform well on link prediction but struggle with unseen entities, relations, and especially literals, limiting their use in dynamic, heterog…
Semantics-Aware Caching for Concept Learning
Louis Mozart Kamdem Teyou, Caglar Demir, Axel-Cyrille Ngonga Ngomo
Concept learning is a form of supervised machine learning that operates on knowledge bases in description logics. State-of-the-art concept learners often rely on an iterative searc…