Knowledge Graph-Driven Resource Identification and Utilization of Salvia-Derived Biomass

Authors

  • Isaac Johansson Department of Computer Science, Binghamton University, Binghamton, NY, USA.
  • Walid Ray Department of Computer Science, University of Central Florida, Orlando, FL, USA.

Keywords:

knowledge graph; Salvia-derived biomass; resource identification; ontology engineering; sustainable bioprocessing; data governance

Abstract

The sustainable identification and utilization of Salvia-derived biomass require coordinated interpretation of highly heterogeneous data across taxonomy, phytochemistry, cell wall composition, agronomic practice, extraction processes, and supply chain logistics. Knowledge graphs provide a scalable semantic framework for representing such distributed data as interconnected entities and relations, enabling explainable resource identification and decision support in biomass valorization. This paper presents a system-level analysis of knowledge graph-driven approaches for Salvia-derived biomass resource identification and utilization. It examines architectural patterns, ontology engineering, data harmonization, and reasoning services from an infrastructure perspective rather than from a single algorithmic viewpoint. The discussion emphasizes structural trade-offs among semantic expressiveness, scalability, data quality, provenance, and governance. It further considers fairness, data sovereignty, institutional arrangements, and policy implications for sustainable bioeconomy systems. The paper develops a conceptual foundation grounded in contemporary knowledge graph research and biomass conversion literature. By integrating taxonomic marker data with process and logistics knowledge, knowledge graph systems can transform fragmented biomass information into actionable knowledge for selecting, processing, and valorizing Salvia-derived resources while maintaining robustness, equity, and long-term sustainability.

References

1. Nickel, M., Murphy, K., Tresp, V., & Gabrilovich, E. (2016). A review of relational machine learning for knowledge graphs. Proceedings of the IEEE, 104(1), 11–33.

2. Hogan, A., Blomqvist, E., Cochez, M., d’Amato, C., de Melo, G., Gutierrez, C., Kirrane, S., Labra Gayo, J. E., Navigli, R., Neumaier, S., Ngonga Ngomo, A.-C., Polleres, A., Rashid, S. M., Rula, A., Schmelzeisen, L., Sequeda, J., Staab, S., & Zimmermann, A. (2021). Knowledge graphs. ACM Computing Surveys, 54(4), 1–37.

3. Vrandečić, D., & Krötzsch, M. (2014). Wikidata: A free collaborative knowledgebase. Communications of the ACM, 57(10), 78–85.

4. Lehmann, J., Isele, R., Jakob, M., Jentzsch, A., Kontokostas, D., Mendes, P. N., Hellmann, S., Morsey, M., van Kleef, P., Auer, S., & Bizer, C. (2015). DBpedia - A large-scale, multilingual knowledge base extracted from Wikipedia. Semantic Web, 6(2), 167–195.

5. Paulheim, H. (2017). Knowledge graph refinement: A survey of approaches and evaluation methods. Semantic Web, 8(3), 489–508.

6. Noy, N. F., & McGuinness, D. L. (2001). Ontology development 101: A guide to creating your first ontology. Stanford Knowledge Systems Laboratory Technical Report KSL-01-05.

7. Musen, M. A. (2015). The Protégé project: A look back and a look forward. AI Matters, 1(4), 4–12.

8. Smith, B., Ashburner, M., Rosse, C., Bard, J., Bug, W., Ceusters, W., Goldberg, L. J., Eilbeck, K., Ireland, A., Mungall, C. J., Leontis, N., Rocca-Serra, P., Ruttenberg, A., Sansone, S.-A., Scheuermann, R. H., Shah, N., Whetzel, P. L., & Lewis, S. (2007). The OBO Foundry: Coordinated evolution of ontologies to support biomedical data integration. Nature Biotechnology, 25(11), 1251–1255.

9. Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., Gonzalez-Beltran, A., Gray, A. J. G., Groth, P., Goble, C., Grethe, J. S., Heringa, J., ’t Hoen, P. A. C., Hooft, R., Kuhn, T., Kok, R., Kok, J., Lusher, S. J., Martone, M. E., Mons, A., Packer, A. L., Persson, B., Rocca-Serra, P., Roos, M., van Schaik, R., Sansone, S.-A., Schultes, E., Sengstag, T., Slater, T., Strawn, G., Swertz, M. A., Thompson, M., van der Lei, J., van Mulligen, E., Velterop, J., Waagmeester, A., Wittenburg, P., Wolstencroft, K., Zhao, J., & Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 160018.

10. Zhao, K., Li, G., Li, C., Jiang, T., Wang, W., & Yang, Z. (2026). Identification Markers for Salvia miltiorrhiza and Its Close Relatives Based on Cell Wall Component Characteristics. Engineered Science, 40, 2122.

11. Wang, X., Morris-Natschke, S. L., & Lee, K. H. (2007). New developments in the chemistry and biology of the bioactive constituents of Tanshen. Medicinal Research Reviews, 27(1), 133–148.

12. Ragauskas, A. J., Williams, C. K., Davison, B. H., Britovsek, G., Cairney, J., Eckert, C. A., Frederick, W. J., Hallett, J. P., Leak, D. J., Liotta, C. L., Mielenz, J. R., Murphy, R., Templer, R., & Tschaplinski, T. (2006). The path forward for biofuels and biomaterials. Science, 311(5760), 484–489.

13. Mohan, D., Pittman, C. U., & Steele, P. H. (2006). Pyrolysis of wood/biomass for bio-oil: A critical review. Energy & Fuels, 20(3), 848–889.

14. Sowa, J. F. (2000). Knowledge representation: Logical, philosophical, and computational foundations. Course Technology.

15. Bizer, C., Heath, T., & Berners-Lee, T. (2009). Linked Data - The story so far. International Journal on Semantic Web and Information Systems, 5(3), 1–22.

16. Zaveri, A., Rula, A., Maurino, A., Pietrobon, R., Lehmann, J., & Auer, S. (2016). Quality assessment for Linked Data: A survey. Semantic Web, 7(1), 63–93.

17. Noy, N., Gao, Y., Jain, A., Narayanan, A., Patterson, A., & Taylor, J. (2019). Industry-scale knowledge graphs: Lessons and challenges. Communications of the ACM, 62(8), 36–43.

18. McKendry, P. (2002). Energy production from biomass (part 1): Overview of biomass. Bioresource Technology, 83(1), 37–46.

19. Janssen, M., Charalabidis, Y., & Zuiderwijk, A. (2012). Benefits, adoption barriers and myths of open data and open government. Information Systems Management, 29(4), 258–268.

20. Ostrom, E. (1990). Governing the commons: The evolution of institutions for collective action. Cambridge University Press.

Downloads

Published

2026-08-30

How to Cite

Isaac Johansson, & Walid Ray. (2026). Knowledge Graph-Driven Resource Identification and Utilization of Salvia-Derived Biomass. International Journal of Clinical and Translational Medicine, 1(1). Retrieved from https://ijctmed.org/index.php/home/article/view/204

Issue

Vol. 1 No. 1 (2026): International Journal of Clinical and Translational Medicine

Section

Articles

License

Copyright (c) 2026 International Journal of Clinical and Translational Medicine

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.