most citedExploring Large Language Models as a Source of Common-Sense Knowledge for Robots

4 citations · 4 across the 3 of their papers we have counts for

collaborators

5 papers

cs.AI2025

Knowledge Graph Completion for Action Prediction on Situational Graphs -- A Case Study on Household Tasks

Mariam Arustashvili, Jörg Deigmöller, Heiko Paulheim

Knowledge Graphs are used for various purposes, including business applications, biomedical analyses, or digital twins in industry 4.0. In this paper, we investigate knowledge grap…

cs.AI2025

A Grounded Memory System For Smart Personal Assistants

Felix Ocker, Jörg Deigmöller, Pavel Smirnov +1

A wide variety of agentic AI applications - ranging from cognitive assistants for dementia patients to robotics - demand a robust memory system grounded in reality. In this paper,…

cs.CL2025

TRAVELER: A Benchmark for Evaluating Temporal Reasoning across Vague, Implicit and Explicit References

Svenja Kenneweg, Jörg Deigmöller, Philipp Cimiano +1

Understanding and resolving temporal references is essential in Natural Language Understanding as we often refer to the past or future in daily communication. Although existing ben…

cs.CL2025

A Factorized Probabilistic Model of the Semantics of Vague Temporal Adverbials Relative to Different Event Types

Svenja Kenneweg, Jörg Deigmöller, Julian Eggert +1

Vague temporal adverbials, such as recently, just, and a long time ago, describe the temporal distance between a past event and the utterance time but leave the exact duration unde…

cs.RO2023★ 4 cited

Exploring Large Language Models as a Source of Common-Sense Knowledge for Robots

Felix Ocker, Jörg Deigmöller, Julian Eggert

Service robots need common-sense knowledge to help humans in everyday situations as it enables them to understand the context of their actions. However, approaches that use ontolog…