Knowledge Graph Generation and Enabling Multidimensional Analytics on Bangladesh Agricultural Data

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, open data portal, 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 Findable, Accessible, Interoperable, and Reusable (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 multidimensionally integrates Bangladesh agricultural data. 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, correctness, and data-driven analysis. Our federated data analyses recommend a strategic approach focused on decreasing CO2 emissions, fostering economic growth, and promoting sustainable forestry.

Keywords

• Agriculture
• data-driven analysis
• FAIR principles
• knowledge graph
• linked data
• multidimensional analysis