Patterns for Representing Knowledge Graphs to Communicate Situational Knowledge of Service Robots
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
Service robots are envisioned to be adaptive to their working environment\nbased on situational knowledge.
Benchmark evidence is limited
Evidence graph: 3 refs, 3 links.
Utility signals: depth 100/100, grounding 85/100, status high.
Implementation
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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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Hardware requirements
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Repositories and ecosystem
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- prakhar1989/awesome-courses Adjacent · Confidence: Medium · 70,625 stars
Matches contextual method/domain keyword: computer science
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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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