Mediaboss Marketing

This blog is part of AI for Data, Data for AI, a series aiming to unwrap, explain and foster the intersection of artificial intelligence and data. This post is the third installment of the seriesfor further reading, here are the first and second installments.

Data is essential for generating knowledge and informing policies. Organizations that produce large volumes of diverse data face challenges in managing and disseminating it effectively. One major challenge is ensuring users can easily find the most relevant data for their needs, a problem known as data discoverability.

Organizations like the World Bank have systems to make their data assets discoverable. Traditionally, these systems use lexical or keyword search applications, indexing available metadata to enable data discovery through search terms. However, this approach limits discovery to the keywords in the accompanying metadata documentation, returning nothing beyond those terms.

Artificial intelligence (AI), primarily large language models (LLMs), can enhance data systems to ensure relevant and timely data are discoverable. With richer metadata and taking advantage of AI-enabled solutions, semantic search, hybrid search, knowledge graphs, and recommendation systems can be utilized.

In this post, we explore how simple AI applications can overcome the limitations of keyword-based search. We also discuss AI-enabled techniques that improve our understanding of users' information needs, leading to a better data search experience.

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Beyond keywords: AI-driven approaches to improve data discoverability - World Bank