TAGIFY: LLM-powered Tagging Interface for Improved Data Findability on OGD portals

TL;DR

This paper introduces Tagify, an LLM-powered interface designed to automate dataset tagging on OGD portals, enhancing data findability and accessibility by generating relevant tags in multiple languages.

Contribution

The paper presents a novel LLM-based tagging interface that automates dataset tagging on OGD portals, addressing current challenges in data discoverability.

Findings

User feedback indicates improved tagging efficiency.

Automated tags increase dataset discoverability.

Multilingual tagging supports diverse user needs.

Abstract

Efforts directed towards promoting Open Government Data (OGD) have gained significant traction across various governmental tiers since the mid-2000s. As more datasets are published on OGD portals, finding specific data becomes harder, leading to information overload. Complete and accurate documentation of datasets, including association of proper tags with datasets is key to improving dataset findability and accessibility. Analysis conducted on the Estonian Open Data Portal, revealed that 11% datasets have no associated tags, while 26% had only one tag assigned to them, which underscores challenges in data findability and accessibility within the portal, which, according to the recent Open Data Maturity Report, is considered trend-setter. The aim of this study is to propose an automated solution to tagging datasets to improve data findability on OGD portals. This paper presents Tagify - a prototype of tagging interface that employs large language models (LLM) such as GPT-3.5-turbo and GPT-4 to automate dataset tagging, generating tags for datasets in English and Estonian, thereby augmenting metadata preparation by data publishers and improving data findability on OGD portals by data users. The developed solution was evaluated by users and their feedback was collected to define an agenda for future prototype improvements.