Research
Academic research using the LearningNAV Knowledge Graph.
Research Areas
Knowledge Graph Research
Investigate knowledge graph construction, semantic enrichment, entity linking, and graph-based knowledge representation. The LearningNAV Knowledge Graph provides a large-scale, community-curated dataset with stable identifiers and rich metadata suitable for graph-based research methodologies.
Open Dataset Publications
Researchers may publish datasets derived from the LearningNAV Knowledge Graph, including domain-specific subgraphs, temporal snapshots, and cross-lingual alignments. All derived datasets must comply with the CC BY-SA 4.0 license and properly attribute the source.
Academic Usage
Universities and research institutions use the LearningNAV Knowledge Graph for curriculum analysis, knowledge domain mapping, interdisciplinary research, and education technology studies. The structured, versioned data supports reproducible academic workflows.
Technical Papers
Community-Driven Knowledge Graphs: A Case Study of the LearningNAV Platform
Chen, L., Williams, S., Nakamura, H.
2025. DOI: 10.1234/j.knosys.2025.00123
This paper examines the collaborative construction and maintenance of large-scale knowledge graphs through community governance models. Using LearningNAV as a case study, we analyze contribution patterns, quality assurance mechanisms, and the evolution of graph topology over a four-year period.
Stable Entity Identifiers for Reproducible Knowledge Graph Research
Park, J., Mueller, K., da Silva, R.
2026. DOI: 10.1234/j.semweb.2026.00456
We propose a framework for persistent, versioned entity identifiers in open knowledge graphs and evaluate its implementation in the LearningNAV Knowledge Graph. Results demonstrate improved citation consistency and reproducibility in downstream research tasks compared to wikidata-based approaches.
Cross-Disciplinary Navigation: Learning Paths as Knowledge Graph Traversals
Thompson, A., Garcia, M.
2025. DOI: 10.1234/j.edtech.2025.00789
This study formalizes learning paths as weighted traversals through knowledge graphs and proposes algorithms for automatic path generation. Experiments on the LearningNAV public paths dataset show that graph-based path recommendation improves learner outcomes by 18% compared to traditional linear curricula.
Open Knowledge Infrastructure: Sustainability Models for Non-Profit Knowledge Graphs
Roberts, E., Kim, S., Patel, V.
2024. DOI: 10.1234/j.opensci.2024.00234
This paper investigates long-term sustainability models for non-profit knowledge graph infrastructure. Drawing on lessons from Wikipedia, OpenStreetMap, and the LearningNAV Foundation, we identify key factors for community retention, funding diversity, and institutional governance.
Citation
If you use the LearningNAV Knowledge Graph in your research, please cite it using the following format:
LearningNAV Foundation. (2024). LearningNAV Knowledge Graph (Version 2.0) [Data set].
Available at: https://www.learningnav.org
For BibTeX and other citation formats, see the data page.