# Patterns for Representing Knowledge Graphs to Communicate Situational Knowledge of Service Robots

arXiv: https://arxiv.org/abs/2101.10602 · HTML: https://mlsanity.com/paper/2101.10602/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2021.

## Built on by (most cited first, 7 of 7)

- [Safety Control of Service Robots with LLMs and Embodied Knowledge Graphs](https://mlsanity.com/paper/2405.17846/) (2024)
- [HealthGenie: Empowering Users with Healthy Dietary Guidance through Knowledge Graph and Large Language Models](https://mlsanity.com/paper/2504.14594/) (2025)
- [The Discovery Engine: A Framework for AI-Driven Synthesis and Navigation of Scientific Knowledge Landscapes](https://mlsanity.com/paper/2505.17500/) (2025)
- [GenComUI: Exploring Generative Visual Aids as Medium to Support Task-Oriented Human-Robot Communication](https://mlsanity.com/paper/2502.10678/) (2025)
- [Symbiotic Child Emotional Support with Social Robots and Temporal Knowledge Graphs](https://mlsanity.com/paper/2205.13229/) (2022)
- [Knowledge Graph Completion for Action Prediction on Situational Graphs -- A Case Study on Household Tasks](https://mlsanity.com/paper/2508.13675/) (2025)
- [A Taxonomy of Conceptual Alignment in Human-Robot Dialogue](https://mlsanity.com/paper/2606.22360/) (2026)

## Abstract (arXiv)

Service robots are envisioned to be adaptive to their working environment based on situational knowledge. Recent research focused on designing visual representation of knowledge graphs for expert users. However, how to generate an understandable interface for non-expert users remains to be explored. In this paper, we use knowledge graphs (KGs) as a common ground for knowledge exchange and develop a pattern library for designing KG interfaces for non-expert users. After identifying the types of robotic situational knowledge from the literature, we present a formative study in which participants used cards to communicate the knowledge for given scenarios. We iteratively coded the results and identified patterns for representing various types of situational knowledge. To derive design recommendations for applying the patterns, we prototyped a lab service robot and conducted Wizard-of-Oz testing. The patterns and recommendations could provide useful guidance in designing knowledge-exchange interfaces for robots.