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

Shengchen Zhang, Zixuan Wang, Chaoran Chen, Yi Dai, Lyumanshan Ye +1 morePublished May 6, 2021
DOI Publisher
Researcher verdict
Context only
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Benchmark evidence
Thin evidence
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Time to first repro
A few days
Plan setup time
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1
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Abstract

Domain fit: AI-adjacent · Paper appears method- or tooling-adjacent to AI workflows with partial ecosystem coverage.

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

Results and benchmarks

Freshness tier: cold
Service robots are envisioned to be adaptive to their working environment\nbased on situational knowledge.

Implementation

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Implementation evidence summary
Confidence: medium

prakhar1989/awesome-courses is the closest maintained adjacent implementation (Matches contextual method/domain keyword: computer science). It is not paper-verified; validate algorithm and evaluation setup against the paper before trusting reported metrics. Community adoption signal: 70625 GitHub stars.

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Reproduction readiness

Time to first repro: days
Last checked: Aug 24, 2026

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Repositories and ecosystem

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Research context

9

Citations

55

References

Tasks

Computer science, Human–computer interaction, Robot, Formative assessment, Service (business), Knowledge management, Open Knowledge Base Connectivity, Knowledge engineering

Methods

None detected

Domains

Artificial intelligence, Computer Vision and Pattern Recognition

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