Bangladesh Agricultural Knowledge Graph: Enabling Semantic Integration and Data-driven Analysis--Full Version

• URL: http://arxiv.org/abs/2403.11920v2
• Date: Tue, 19 Mar 2024 04:40:43 GMT
• Title: Bangladesh Agricultural Knowledge Graph: Enabling Semantic Integration and Data-driven Analysis--Full Version
• Authors: Rudra Pratap Deb Nath, Tithi Rani Das, Tonmoy Chandro Das, S. M. Shafkat Raihan,
• Abstract summary: We develop a federated knowledge graph that integrates agriculture data in Bangladesh. BDAKG incorporates multidimensional semantics, is linked with external knowledge graphs, is compatible with OLAP, and adheres to the FAIR principles.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In Bangladesh, agriculture is a crucial driver for addressing Sustainable Development Goal 1 (No Poverty) and 2 (Zero Hunger), playing a fundamental role in the economy and people's livelihoods. To enhance the sustainability and resilience of the agriculture industry through data-driven insights, the Bangladesh Bureau of Statistics and other organizations consistently collect and publish agricultural data on the Web. Nevertheless, the current datasets encounter various challenges: 1) they are presented in an unsustainable, static, read-only, and aggregated format, 2) they do not conform to the Findability, Accessibility, Interoperability, and Reusability (FAIR) principles, and 3) they do not facilitate interactive analysis and integration with other data sources. In this paper, we present a thorough solution, delineating a systematic procedure for developing BDAKG: a knowledge graph that semantically and analytically integrates agriculture data in Bangladesh. BDAKG incorporates multidimensional semantics, is linked with external knowledge graphs, is compatible with OLAP, and adheres to the FAIR principles. Our experimental evaluation centers on evaluating the integration process and assessing the quality of the resultant knowledge graph in terms of completeness, timeliness, FAIRness, OLAP compatibility and data-driven analysis. Our federated data analysis recommend a strategic approach focused on decreasing CO$_2$ emissions, fostering economic growth, and promoting sustainable forestry.

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