Comparing human and automatic thesaurus mapping approaches in the agricultural domain

TL;DR

This paper compares human and automatic thesaurus mapping methods in agriculture, highlighting their strengths, limitations, and potential for complementarity to improve knowledge organization systems.

Contribution

It provides an empirical comparison of human and machine mapping approaches for AGROVOC, identifying their respective advantages and challenges.

Findings

Automatic mapping has limitations with complex cases

Human mapping is more accurate but labor-intensive

Combining both approaches can enhance mapping quality

Abstract

Knowledge organization systems (KOS), like thesauri and other controlled vocabularies, are used to provide subject access to information systems across the web. Due to the heterogeneity of these systems, mapping between vocabularies becomes crucial for retrieving relevant information. However, mapping thesauri is a laborious task, and thus big efforts are being made to automate the mapping process. This paper examines two mapping approaches involving the agricultural thesaurus AGROVOC, one machine-created and one human created. We are addressing the basic question "What are the pros and cons of human and automatic mapping and how can they complement each other?" By pointing out the difficulties in specific cases or groups of cases and grouping the sample into simple and difficult types of mappings, we show the limitations of current automatic methods and come up with some basic recommendations on what approach to use when.